{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VVSOpjzJl_cx"
   },
   "source": [
    "# 🤗 Welcome to AdalFlow!\n",
    "## The library to build & auto-optimize any LLM task pipelines\n",
    "\n",
    "Thanks for trying us out, we're here to provide you with the best LLM application development experience you can dream of 😊 any questions or concerns you may have, [come talk to us on discord,](https://discord.gg/ezzszrRZvT) we're always here to help!\n",
    "\n",
    "\n",
    "# Quick Links\n",
    "\n",
    "Github repo: https://github.com/SylphAI-Inc/AdalFlow\n",
    "\n",
    "Full Tutorials: https://adalflow.sylph.ai/index.html#.\n",
    "\n",
    "Deep dive on each API: check out the [developer notes](https://adalflow.sylph.ai/tutorials/index.html).\n",
    "\n",
    "Common use cases along with the auto-optimization:  check out [Use cases](https://adalflow.sylph.ai/use_cases/index.html).\n",
    "\n",
    "# Outline\n",
    "\n",
    "*Note: As training can consume tokens fast, and the notebook runtime will reset everytime you use, it might be better for you to learn training in your local editor.*\n",
    "\n",
    "This is a quick introduction of AdalFlow on question answering use case end to end\n",
    "\n",
    "* Trainable Task pipeline with trainable parameters\n",
    "* Create AdalComponent for your task pipeline\n",
    "* Use Trainer to diagnose, debug, and to train.\n",
    "\n",
    "You can find all source code here: https://github.com/SylphAI-Inc/AdalFlow/tree/main/use_cases/question_answering/bhh_object_count\n",
    "\n",
    "**Here is the more detailed tutorial for the code here: https://adalflow.sylph.ai/use_cases/question_answering.html**\n",
    "\n",
    "\n",
    "# Installation\n",
    "\n",
    "1. Use `pip` to install the `adalflow` Python package. We will need `openai`, `groq`, and `faiss`(cpu version) from the extra packages.\n",
    "\n",
    "  ```bash\n",
    "  pip install adalflow[openai,groq,faiss-cpu]\n",
    "  ```\n",
    "2. Setup  `openai` and `groq` API key in the environment variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "THTvmhjgfiHE"
   },
   "outputs": [],
   "source": [
    "from IPython.display import clear_output\n",
    "\n",
    "!pip install -U adalflow[openai,groq,datasets]\n",
    "\n",
    "clear_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KapUyHMM07pJ"
   },
   "source": [
    "## Set Environment Variables\n",
    "\n",
    "Run the following code and pass your api key.\n",
    "\n",
    "Note: for normal `.py` projects, follow our [official installation guide](https://lightrag.sylph.ai/get_started/installation.html).\n",
    "\n",
    "*Go to [OpenAI](https://platform.openai.com/docs/introduction) and [Groq](https://console.groq.com/docs/) to get API keys if you don't already have.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ONfzF9Puzdd_",
    "outputId": "6a815e21-ab99-463e-c53b-e39ca2ce8f3f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter your OpenAI API key: ··········\n",
      "Please enter your GROQ API key: ··········\n",
      "API keys have been set.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "from getpass import getpass\n",
    "\n",
    "# Prompt user to enter their API keys securely\n",
    "openai_api_key = getpass(\"Please enter your OpenAI API key: \")\n",
    "groq_api_key = getpass(\n",
    "    \"Please enter your GROQ API key, simplly press Enter if you don't have one: \"\n",
    ")\n",
    "\n",
    "\n",
    "# Set environment variables\n",
    "os.environ[\"OPENAI_API_KEY\"] = openai_api_key\n",
    "os.environ[\"GROQ_API_KEY\"] = groq_api_key\n",
    "\n",
    "print(\"API keys have been set.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SfGS7iddtfpj"
   },
   "source": [
    "\n",
    "\n",
    "# 😇 Trainable Task Pipeline\n",
    "\n",
    "We will create a task pipeline consists of a generator, with a customzied template, a customized output parser.\n",
    "\n",
    "Different from our other pipelines where the `prompt_kwargs` values are strings, but here we will use ``Parameter``. And we will set up two parameter, one is of ``ParameterType.PROMPT`` and the other of type ``ParameterType.DEMOS``. The first one will be trained by text-grad and the second will be trained by boostrap few shot optimizer.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nHnvAbO-pXUq"
   },
   "outputs": [],
   "source": [
    "import adalflow as adal\n",
    "import re\n",
    "from typing import Dict, Union\n",
    "import adalflow as adal\n",
    "from adalflow.optim.types import ParameterType\n",
    "\n",
    "\n",
    "@adal.func_to_data_component\n",
    "def parse_integer_answer(answer: str):\n",
    "    \"\"\"A function that parses the last integer from a string using regular expressions.\"\"\"\n",
    "    try:\n",
    "        # Use regular expression to find all sequences of digits\n",
    "        numbers = re.findall(r\"\\d+\", answer)\n",
    "        if numbers:\n",
    "            # Get the last number found\n",
    "            answer = int(numbers[-1])\n",
    "        else:\n",
    "            answer = -1\n",
    "    except ValueError:\n",
    "        answer = -1\n",
    "\n",
    "    return answer\n",
    "\n",
    "\n",
    "few_shot_template = r\"\"\"<START_OF_SYSTEM_PROMPT>\n",
    "{{system_prompt}}\n",
    "{# Few shot demos #}\n",
    "{% if few_shot_demos is not none %}\n",
    "Here are some examples:\n",
    "{{few_shot_demos}}\n",
    "{% endif %}\n",
    "<END_OF_SYSTEM_PROMPT>\n",
    "<START_OF_USER>\n",
    "{{input_str}}\n",
    "<END_OF_USER>\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "class ObjectCountTaskPipeline(adal.Component):\n",
    "    def __init__(self, model_client: adal.ModelClient, model_kwargs: Dict):\n",
    "        super().__init__()\n",
    "\n",
    "        system_prompt = adal.Parameter(\n",
    "            data=\"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\",\n",
    "            role_desc=\"To give task instruction to the language model in the system prompt\",\n",
    "            requires_opt=True,\n",
    "            param_type=ParameterType.PROMPT,\n",
    "        )\n",
    "        few_shot_demos = adal.Parameter(\n",
    "            data=None,\n",
    "            role_desc=\"To provide few shot demos to the language model\",\n",
    "            requires_opt=True,  # Changed to True for few-shot learning\n",
    "            param_type=ParameterType.DEMOS,\n",
    "        )\n",
    "\n",
    "        self.llm_counter = adal.Generator(\n",
    "            model_client=model_client,\n",
    "            model_kwargs=model_kwargs,\n",
    "            template=few_shot_template,\n",
    "            prompt_kwargs={\n",
    "                \"system_prompt\": system_prompt,\n",
    "                \"few_shot_demos\": few_shot_demos,\n",
    "            },\n",
    "            output_processors=parse_integer_answer,\n",
    "            use_cache=True,\n",
    "        )\n",
    "\n",
    "    def bicall(\n",
    "        self, question: str, id: str = None\n",
    "    ) -> Union[adal.GeneratorOutput, adal.Parameter]:\n",
    "        output = self.llm_counter(prompt_kwargs={\"input_str\": question}, id=id)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AvZJjdzZa0cT"
   },
   "source": [
    "Next, we will run this pipeline in both train and eval mode.\n",
    "\n",
    "#### Eval mode with GeneratorOutput\n",
    "\n",
    "Eval mode will output ``GeneratorOutput``.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gks3yS8hcR6_"
   },
   "source": [
    "\n",
    "#### Train mode with different form of output\n",
    "\n",
    "Train mode will return ``Parameter``, where the `data` field will be the `raw_response`` from the GeneratorOutput, and we put the full GeneratorOutput at the ``full_response`` in the parameter.\n",
    "\n",
    "As the `data` field of the `Parameter` directly communicate with the Optimizer, which are an LLM itself, its better than they understand exactly the string response itself instead of the parsed one.\n",
    "\n",
    "Later you will see that we also use ``eval_input`` of the parameter to communicate with the `LossFunction` as that need the parsed final output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eqQSFnZOpfWJ",
    "outputId": "05b5fc83-09d1-45f4-aacc-6d460fbdd7bd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:adalflow.core.generator:Error copying the prompt_kwargs: 'prompt' is not a valid ParameterType\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-3.5-turbo.db\n",
      "ObjectCountTaskPipeline(\n",
      "  (llm_counter): Generator(\n",
      "    model_kwargs={'model': 'gpt-3.5-turbo', 'max_tokens': 2000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "    (prompt): Prompt(\n",
      "      template: <START_OF_SYSTEM_PROMPT>\n",
      "      {{system_prompt}}\n",
      "      {# Few shot demos #}\n",
      "      {% if few_shot_demos is not none %}\n",
      "      Here are some examples:\n",
      "      {{few_shot_demos}}\n",
      "      {% endif %}\n",
      "      <END_OF_SYSTEM_PROMPT>\n",
      "      <START_OF_USER>\n",
      "      {{input_str}}\n",
      "      <END_OF_USER>\n",
      "      , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': None}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "    )\n",
      "    (model_client): OpenAIClient()\n",
      "    (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "from adalflow.components.model_client.openai_client import OpenAIClient\n",
    "from adalflow.components.model_client.groq_client import GroqAPIClient\n",
    "\n",
    "\n",
    "if len(os.environ[\"OPENAI_API_KEY\"]) > 1:\n",
    "    gpt_3_model = {\n",
    "        \"model_client\": OpenAIClient(),\n",
    "        \"model_kwargs\": {\n",
    "            \"model\": \"gpt-3.5-turbo\",\n",
    "            \"max_tokens\": 2000,\n",
    "            \"temperature\": 0.0,\n",
    "            \"top_p\": 0.99,\n",
    "            \"frequency_penalty\": 0,\n",
    "            \"presence_penalty\": 0,\n",
    "            \"stop\": None,\n",
    "        },\n",
    "    }\n",
    "    gpt_4o_model = {\n",
    "        \"model_client\": OpenAIClient(),\n",
    "        \"model_kwargs\": {\n",
    "            \"model\": \"gpt-4o\",\n",
    "            \"max_tokens\": 4000,\n",
    "            \"temperature\": 0.0,\n",
    "            \"top_p\": 0.99,\n",
    "            \"frequency_penalty\": 0,\n",
    "            \"presence_penalty\": 0,\n",
    "            \"stop\": None,\n",
    "        },\n",
    "    }\n",
    "\n",
    "if len(os.environ[\"GROQ_API_KEY\"]) > 1:\n",
    "    llama_3_1_model = {\n",
    "        \"model_client\": GroqAPIClient(),\n",
    "        \"model_kwargs\": {\"model\": \"llama-3.1-8b-instant\"},\n",
    "    }\n",
    "\n",
    "\n",
    "question = \"I have a flute, a piano, a trombone, four stoves, a violin, an accordion, a clarinet, a drum, two lamps, and a trumpet. How many musical instruments do I have?\"\n",
    "task_pipeline = ObjectCountTaskPipeline(**gpt_3_model)\n",
    "print(task_pipeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DE1xNdYvcXw8",
    "outputId": "25844c2a-5d4c-4c68-8ca5-38b79ca5b398"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GeneratorOutput(id='1', data=8, error=None, usage=CompletionUsage(completion_tokens=77, prompt_tokens=113, total_tokens=190), raw_response='To find the total number of musical instruments you have, you simply need to count the individual instruments you listed. \\n\\nYou have:\\n- Flute\\n- Piano\\n- Trombone\\n- Violin\\n- Accordion\\n- Clarinet\\n- Drum\\n- Trumpet\\n\\nCounting each of these instruments, we get a total of 8 musical instruments.\\n\\nAnswer: 8', metadata=None)\n"
     ]
    }
   ],
   "source": [
    "answer = task_pipeline(question, id=\"1\")\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AGUlUsGxcaby",
    "outputId": "8c8588fe-2994-4d9e-c2d1-26453141f43f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter(name=Generator_output, requires_opt=True, param_type=generator_output (The output of the generator.), role_desc=Output from (llm) Generator, data=To find the total number of musical instruments you have, you simply need to count the individual instruments you listed. \n",
      "\n",
      "You have:\n",
      "- Flute\n",
      "- Piano\n",
      "- Trombone\n",
      "- Violin\n",
      "- Accordion\n",
      "- Clarinet\n",
      "- Drum\n",
      "- Trumpet\n",
      "\n",
      "Counting each of these instruments, we get a total of 8 musical instruments.\n",
      "\n",
      "Answer: 8, predecessors={Parameter(name=To_provide, requires_opt=True, param_type=demos (A few examples to guide the language model.), role_desc=To provide few shot demos to the language model, data=None, predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), Parameter(name=To_give_ta, requires_opt=True, param_type=prompt (Instruction to the language model on task, data, and format.), role_desc=To give task instruction to the language model in the system prompt, data=You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value., predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={})}, gradients=[],            raw_response=None, input_args={'prompt_kwargs': {'system_prompt': Parameter(name=To_give_ta, requires_opt=True, param_type=prompt (Instruction to the language model on task, data, and format.), role_desc=To give task instruction to the language model in the system prompt, data=You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value., predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), 'few_shot_demos': Parameter(name=To_provide, requires_opt=True, param_type=demos (A few examples to guide the language model.), role_desc=To provide few shot demos to the language model, data=None, predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), 'input_str': 'I have a flute, a piano, a trombone, four stoves, a violin, an accordion, a clarinet, a drum, two lamps, and a trumpet. How many musical instruments do I have?'}, 'model_kwargs': {'model': 'gpt-3.5-turbo', 'max_tokens': 2000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, traces={})\n",
      "full_response: GeneratorOutput(id=None, data=8, error=None, usage=CompletionUsage(completion_tokens=77, prompt_tokens=113, total_tokens=190), raw_response='To find the total number of musical instruments you have, you simply need to count the individual instruments you listed. \\n\\nYou have:\\n- Flute\\n- Piano\\n- Trombone\\n- Violin\\n- Accordion\\n- Clarinet\\n- Drum\\n- Trumpet\\n\\nCounting each of these instruments, we get a total of 8 musical instruments.\\n\\nAnswer: 8', metadata=None)\n"
     ]
    }
   ],
   "source": [
    "# set it to train mode\n",
    "task_pipeline.train()\n",
    "answer = task_pipeline(question, id=\"1\")\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-Gvfcy2IcgWx"
   },
   "source": [
    "### Load Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AYBIGsIHpjMe"
   },
   "outputs": [],
   "source": [
    "from adalflow.datasets.big_bench_hard import BigBenchHard\n",
    "from adalflow.utils.data import subset_dataset\n",
    "\n",
    "\n",
    "def load_datasets(max_samples: int = None):\n",
    "    \"\"\"Load the dataset\"\"\"\n",
    "    train_data = BigBenchHard(split=\"train\")\n",
    "    val_data = BigBenchHard(split=\"val\")\n",
    "    test_data = BigBenchHard(split=\"test\")\n",
    "\n",
    "    # Limit the number of samples\n",
    "    if max_samples:\n",
    "        train_data = subset_dataset(train_data, max_samples)\n",
    "        val_data = subset_dataset(val_data, max_samples)\n",
    "        test_data = subset_dataset(test_data, max_samples)\n",
    "\n",
    "    return train_data, val_data, test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "asw-pJrid8ly",
    "outputId": "31807c34-0de9-45e5-ebdd-778aa5313802"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example(id='d3f33ded-170a-4b87-9b0b-987d5fb7b817', question='I have a cauliflower, a stalk of celery, a cabbage, and a garlic. How many vegetables do I have?', answer='4')\n"
     ]
    }
   ],
   "source": [
    "# check the datasets\n",
    "\n",
    "train_data, val_data, test_data = load_datasets(max_samples=2)\n",
    "print(train_data[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VAVtXE9xeEHt"
   },
   "source": [
    "### Soft link to AdalFlow default file path\n",
    "\n",
    "Lets' match the default to the current project, so that you can see the downloaded data and later the checkpoints of the training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1SaKH6dkeWus"
   },
   "outputs": [],
   "source": [
    "! ln -s /root/.adalflow /content/adalflow\n",
    "\n",
    "# go to files then you will see a folder named as adalflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YWZzOvAHenME"
   },
   "source": [
    "# 😊 AdalComponent to define everything we need to train\n",
    "\n",
    "1. We need `backward_engine_model_config`` for ``backward_engine`` to compute gradient.\n",
    "\n",
    "2. We need ``text_optimizer_model_config`` for the `text optimizer` for propose new prompts.\n",
    "\n",
    "3. For the demo optimizer, we need a `teacher_model_config` to config a teacher generator, in this case, it is the `llm_counter`. The teacher will share the same prompt with the `llm_counter` but you can use a more advanced model.\n",
    "\n",
    "In general, we should have all of these parts to use a more advanced model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9QoNoMWD0rgV"
   },
   "source": [
    "## 🧑 Diagnose\n",
    "\n",
    "Diagnose is more of an evaluation, but with detailed logs so that you can manually inspect the wrong output.\n",
    "\n",
    "This one shows the minimum config you need to get the `diagnose` work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6mi7lM3U24Eg"
   },
   "outputs": [],
   "source": [
    "from adalflow.datasets.types import Example\n",
    "from adalflow.eval.answer_match_acc import AnswerMatchAcc\n",
    "\n",
    "\n",
    "class ObjectCountAdalComponent(adal.AdalComponent):\n",
    "    def __init__(self, model_client: adal.ModelClient, model_kwargs: Dict):\n",
    "        task = ObjectCountTaskPipeline(model_client, model_kwargs)\n",
    "        eval_fn = AnswerMatchAcc(type=\"exact_match\").compute_single_item\n",
    "        super().__init__(task=task, eval_fn=eval_fn)\n",
    "\n",
    "    def prepare_task(self, sample: Example):\n",
    "        return self.task.bicall, {\"question\": sample.question, \"id\": sample.id}\n",
    "\n",
    "    def prepare_eval(self, sample: Example, y_pred: adal.GeneratorOutput) -> float:\n",
    "        y_label = -1\n",
    "        if (\n",
    "            y_pred is not None and y_pred.data is not None\n",
    "        ):  # if y_pred and y_pred.data: might introduce bug when the data is 0\n",
    "            y_label = y_pred.data\n",
    "        return self.eval_fn, {\"y\": y_label, \"y_gt\": sample.answer}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eliPeVeM2wcP"
   },
   "outputs": [],
   "source": [
    "def diagnose(\n",
    "    model_client: adal.ModelClient,\n",
    "    model_kwargs: Dict,\n",
    ") -> Dict:\n",
    "\n",
    "    trainset, valset, testset = load_datasets()\n",
    "    # use max_samples=10 to test the code\n",
    "\n",
    "    adal_component = ObjectCountAdalComponent(model_client, model_kwargs)\n",
    "    trainer = adal.Trainer(adaltask=adal_component)\n",
    "    trainer.diagnose(dataset=trainset, split=\"train\")\n",
    "    trainer.diagnose(dataset=valset, split=\"val\")\n",
    "    trainer.diagnose(dataset=testset, split=\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nKl9clcb3dFj",
    "outputId": "676fbb96-c70b-40ab-ea15-93ade1aa9e66"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:adalflow.core.generator:Error copying the prompt_kwargs: 'prompt' is not a valid ParameterType\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-3.5-turbo.db\n",
      "Checkpoint path: /root/.adalflow/ckpt/ObjectCountAdalComponent\n",
      "Save diagnose to /root/.adalflow/ckpt/ObjectCountAdalComponent/diagnose_train\n",
      "Saving traces to /root/.adalflow/ckpt/ObjectCountAdalComponent/diagnose_train\n",
      "all_generators: [('llm_counter', Generator(\n",
      "  model_kwargs={'model': 'gpt-3.5-turbo', 'max_tokens': 2000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': None}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "))]\n",
      "Registered callback for llm_counter, file path: /root/.adalflow/ckpt/ObjectCountAdalComponent/diagnose_train/llm_counter_call.jsonl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 5957.82it/s]\n",
      "Evaluating step(0): 0.88 across 50 samples, Max potential: 0.88: 100%|██████████| 50/50 [00:15<00:00,  3.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sorted_indices: [8, 16, 23, 25, 31, 47, 0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 24, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49]\n",
      "sorted_scores: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "Loading log file: llm_counter_call.jsonl\n",
      "Total error samples: 6\n",
      "Save diagnose to /root/.adalflow/ckpt/ObjectCountAdalComponent/diagnose_val\n",
      "Saving traces to /root/.adalflow/ckpt/ObjectCountAdalComponent/diagnose_val\n",
      "all_generators: [('llm_counter', Generator(\n",
      "  model_kwargs={'model': 'gpt-3.5-turbo', 'max_tokens': 2000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': None}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "))]\n",
      "Registered callback for llm_counter, file path: /root/.adalflow/ckpt/ObjectCountAdalComponent/diagnose_val/llm_counter_call.jsonl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 3203.76it/s]\n",
      "Evaluating step(0): 0.8 across 50 samples, Max potential: 0.8: 100%|██████████| 50/50 [00:15<00:00,  3.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sorted_indices: [1, 2, 5, 10, 24, 36, 38, 42, 44, 47, 0, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 39, 40, 41, 43, 45, 46, 48, 49]\n",
      "sorted_scores: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "Loading log file: llm_counter_call.jsonl\n",
      "Total error samples: 10\n",
      "Save diagnose to /root/.adalflow/ckpt/ObjectCountAdalComponent/diagnose_test\n",
      "Saving traces to /root/.adalflow/ckpt/ObjectCountAdalComponent/diagnose_test\n",
      "all_generators: [('llm_counter', Generator(\n",
      "  model_kwargs={'model': 'gpt-3.5-turbo', 'max_tokens': 2000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': None}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "))]\n",
      "Registered callback for llm_counter, file path: /root/.adalflow/ckpt/ObjectCountAdalComponent/diagnose_test/llm_counter_call.jsonl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading Data: 100%|██████████| 100/100 [00:00<00:00, 5545.09it/s]\n",
      "Evaluating step(0): 0.83 across 100 samples, Max potential: 0.83: 100%|██████████| 100/100 [00:28<00:00,  3.50it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sorted_indices: [7, 18, 19, 20, 23, 24, 25, 43, 58, 59, 63, 74, 75, 79, 85, 97, 99, 0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 21, 22, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 60, 61, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 76, 77, 78, 80, 81, 82, 83, 84, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 98]\n",
      "sorted_scores: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "Loading log file: llm_counter_call.jsonl\n",
      "Total error samples: 17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "diagnose(**gpt_3_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dSu4VQri3y3D"
   },
   "source": [
    "Now, you can go to `/content/adalflow/ckpt/ObjectCountAdalComponent/diagnose_train/stats.json` to view the average score for each split. And also the `diagnose.json` for different errors.\n",
    "\n",
    "Here is the overall score for each split.\n",
    "\n",
    "| Train  | Val| Test |\n",
    "|:--------- |:--------:| ---------:|\n",
    "| 0.88      | 0.8   |    0.83  |\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1vzJyp-W0z7I"
   },
   "source": [
    "## 🐛 Debug"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TmlCvJu804dJ"
   },
   "source": [
    "## ✅ Train\n",
    "\n",
    "Now, let's start training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4TWCn0did6-K"
   },
   "outputs": [],
   "source": [
    "from adalflow.datasets.types import Example\n",
    "\n",
    "\n",
    "class ObjectCountAdalComponent(adal.AdalComponent):  # noqa: F811\n",
    "    def __init__(\n",
    "        self,\n",
    "        model_client: adal.ModelClient,\n",
    "        model_kwargs: Dict,\n",
    "        backward_engine_model_config: Dict,\n",
    "        teacher_model_config: Dict,\n",
    "        text_optimizer_model_config: Dict,\n",
    "    ):\n",
    "        task = ObjectCountTaskPipeline(model_client, model_kwargs)\n",
    "        eval_fn = AnswerMatchAcc(type=\"exact_match\").compute_single_item\n",
    "        loss_fn = adal.EvalFnToTextLoss(\n",
    "            eval_fn=eval_fn,\n",
    "            eval_fn_desc=\"exact_match: 1 if str(y) == str(y_gt) else 0\",\n",
    "        )\n",
    "        super().__init__(task=task, eval_fn=eval_fn, loss_fn=loss_fn)\n",
    "\n",
    "        self.backward_engine_model_config = backward_engine_model_config\n",
    "        self.teacher_model_config = teacher_model_config\n",
    "        self.text_optimizer_model_config = text_optimizer_model_config\n",
    "\n",
    "    def prepare_task(self, sample: Example):\n",
    "        return self.task.bicall, {\"question\": sample.question, \"id\": sample.id}\n",
    "\n",
    "    def prepare_eval(self, sample: Example, y_pred: adal.GeneratorOutput) -> float:\n",
    "        y_label = -1\n",
    "        if (\n",
    "            y_pred is not None and y_pred.data is not None\n",
    "        ):  # if y_pred and y_pred.data: might introduce bug when the data is 0\n",
    "            y_label = y_pred.data\n",
    "        return self.eval_fn, {\"y\": y_label, \"y_gt\": sample.answer}\n",
    "\n",
    "    def prepare_loss(self, sample: Example, pred: adal.Parameter):\n",
    "        # prepare gt parameter\n",
    "        y_gt = adal.Parameter(\n",
    "            name=\"y_gt\",\n",
    "            data=sample.answer,\n",
    "            eval_input=sample.answer,\n",
    "            requires_opt=False,\n",
    "        )\n",
    "\n",
    "        # pred's full_response is the output of the task pipeline which is GeneratorOutput\n",
    "        pred.eval_input = pred.full_response.data\n",
    "        return self.loss_fn, {\"kwargs\": {\"y\": pred, \"y_gt\": y_gt}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dezwX2yn1eQS"
   },
   "outputs": [],
   "source": [
    "def train(\n",
    "    train_batch_size=4,  # larger batch size is not that effective, probably because of llm's lost in the middle\n",
    "    raw_shots: int = 0,\n",
    "    bootstrap_shots: int = 1,\n",
    "    max_steps=1,\n",
    "    num_workers=4,\n",
    "    strategy=\"random\",\n",
    "    optimization_order=\"sequential\",\n",
    "    debug=False,\n",
    "    resume_from_ckpt=None,\n",
    "    exclude_input_fields_from_bootstrap_demos=False,\n",
    "):\n",
    "    adal_component = ObjectCountAdalComponent(\n",
    "        **gpt_3_model,\n",
    "        teacher_model_config=gpt_4o_model,\n",
    "        text_optimizer_model_config=gpt_4o_model,\n",
    "        backward_engine_model_config=gpt_4o_model,\n",
    "    )\n",
    "    print(adal_component)\n",
    "    trainer = adal.Trainer(\n",
    "        train_batch_size=train_batch_size,\n",
    "        adaltask=adal_component,\n",
    "        strategy=strategy,\n",
    "        max_steps=max_steps,\n",
    "        num_workers=num_workers,\n",
    "        raw_shots=raw_shots,\n",
    "        bootstrap_shots=bootstrap_shots,\n",
    "        debug=debug,\n",
    "        weighted_sampling=True,\n",
    "        optimization_order=optimization_order,\n",
    "        exclude_input_fields_from_bootstrap_demos=exclude_input_fields_from_bootstrap_demos,\n",
    "    )\n",
    "    print(trainer)\n",
    "\n",
    "    train_dataset, val_dataset, test_dataset = load_datasets()\n",
    "    trainer.fit(\n",
    "        train_dataset=train_dataset,\n",
    "        val_dataset=val_dataset,\n",
    "        test_dataset=test_dataset,\n",
    "        debug=debug,\n",
    "        resume_from_ckpt=resume_from_ckpt,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NGKYozGt60Pp"
   },
   "source": [
    "We use `Sequential` in default, we will end up with 24 steps in total, 12 for text optimizer and 12 for the demo optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yDwLwL0L7Rsw",
    "outputId": "1b7e413b-a1d3-4388-fc0c-ca4b1c072585"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:adalflow.core.generator:Error copying the prompt_kwargs: 'prompt' is not a valid ParameterType\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-3.5-turbo.db\n",
      "ObjectCountAdalComponent(\n",
      "  eval_fn: compute_single_item, backward_engine: None, backward_engine_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, teacher_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, text_optimizer_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}\n",
      "  (task): ObjectCountTaskPipeline(\n",
      "    (llm_counter): Generator(\n",
      "      model_kwargs={'model': 'gpt-3.5-turbo', 'max_tokens': 2000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "      (prompt): Prompt(\n",
      "        template: <START_OF_SYSTEM_PROMPT>\n",
      "        {{system_prompt}}\n",
      "        {# Few shot demos #}\n",
      "        {% if few_shot_demos is not none %}\n",
      "        Here are some examples:\n",
      "        {{few_shot_demos}}\n",
      "        {% endif %}\n",
      "        <END_OF_SYSTEM_PROMPT>\n",
      "        <START_OF_USER>\n",
      "        {{input_str}}\n",
      "        <END_OF_USER>\n",
      "        , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': None}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "      )\n",
      "      (model_client): OpenAIClient()\n",
      "      (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "    )\n",
      "  )\n",
      "  (loss_fn): EvalFnToTextLoss()\n",
      ")\n",
      "Trainer(\n",
      "  (adaltask): ObjectCountAdalComponent(\n",
      "    eval_fn: compute_single_item, backward_engine: None, backward_engine_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, teacher_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, text_optimizer_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}\n",
      "    (task): ObjectCountTaskPipeline(\n",
      "      (llm_counter): Generator(\n",
      "        model_kwargs={'model': 'gpt-3.5-turbo', 'max_tokens': 2000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "        (prompt): Prompt(\n",
      "          template: <START_OF_SYSTEM_PROMPT>\n",
      "          {{system_prompt}}\n",
      "          {# Few shot demos #}\n",
      "          {% if few_shot_demos is not none %}\n",
      "          Here are some examples:\n",
      "          {{few_shot_demos}}\n",
      "          {% endif %}\n",
      "          <END_OF_SYSTEM_PROMPT>\n",
      "          <START_OF_USER>\n",
      "          {{input_str}}\n",
      "          <END_OF_USER>\n",
      "          , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': None}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "        )\n",
      "        (model_client): OpenAIClient()\n",
      "        (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "      )\n",
      "    )\n",
      "    (loss_fn): EvalFnToTextLoss()\n",
      "  )\n",
      ")\n",
      "raw_shots: 0, bootstrap_shots: 1\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Configuring teacher generator for Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "Teacher generator set: Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "), teacher Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "Teacher generator configured.\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Backward engine configured for all generators.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 6482.70it/s]\n",
      "Evaluating step(0): 0.8 across 50 samples, Max potential: 0.8: 100%|██████████| 50/50 [00:00<00:00, 347.01it/s]\n",
      "Loading Data: 100%|██████████| 100/100 [00:00<00:00, 2017.67it/s]\n",
      "Evaluating step(0): 0.83 across 100 samples, Max potential: 0.83: 100%|██████████| 100/100 [00:00<00:00, 286.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial validation score: 0.8\n",
      "Initial test score: 0.83\n",
      "Checkpoint path: /root/.adalflow/ckpt/ObjectCountAdalComponent\n",
      "save to /root/.adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Step: 1:   0%|          | 0/13 [00:00<?, ?it/s]\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 133.39it/s]\n",
      "Training: 100%|██████████| 4/4 [00:00<00:00, 1489.32it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 262.46it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 12865.96it/s]\n",
      "Training Step: 2:   8%|▊         | 1/13 [00:00<00:01,  8.29it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping batch 0 as acc: 1.0\n",
      "No proposal can improve the subset and full set, go to next step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 384.73it/s]\n",
      "Training: 100%|██████████| 4/4 [00:00<00:00, 927.64it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 754.71it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 12087.33it/s]\n",
      "Training Step: 3:  15%|█▌        | 2/13 [00:00<00:01,  8.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping batch 1 as acc: 1.0\n",
      "No proposal can improve the subset and full set, go to next step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 193.44it/s]\n",
      "Training: 100%|██████████| 4/4 [00:00<00:00, 2761.68it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 810.38it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 11320.66it/s]\n",
      "Training Step: 4:  15%|█▌        | 2/13 [00:00<00:01,  8.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping batch 2 as acc: 1.0\n",
      "No proposal can improve the subset and full set, go to next step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 234.44it/s]\n",
      "Training: 100%|██████████| 4/4 [00:00<00:00, 2487.72it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 1024.88it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 12018.06it/s]\n",
      "Training Step: 5:  31%|███       | 4/13 [00:00<00:00, 11.90it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping batch 3 as acc: 1.0\n",
      "No proposal can improve the subset and full set, go to next step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 133.95it/s]\n",
      "Training: 100%|██████████| 4/4 [00:00<00:00, 4552.84it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 392.05it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 770.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 3\n",
      "Moving batch error size: 1\n",
      "Moving batch acc: 0.75\n",
      "Moving batch correct size: 3\n",
      "Moving batch error size: 1\n",
      "Subset Error size: 1\n",
      "Subset Correct size: 2\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "setting pred name Generator_outputy_pred_3 score to 0.0\n",
      "Subset loss backward time: 5.383355617523193\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 225.14it/s]\n",
      "Evaluating step(4): 1.0 across 3 samples, Max potential: 1.0: 100%|██████████| 3/3 [00:01<00:00,  2.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass subset check: 1.0 > 0.6666666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 445.28it/s]\n",
      "Evaluating step(4): 1.0 across 4 samples, Max potential: 1.0: 100%|██████████| 4/4 [00:01<00:00,  2.67it/s]\n",
      "Proposing:   0%|          | 0/5 [00:03<?, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass full check: 1.0 >= 0.75\n",
      "Done with proposals\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1139.66it/s]\n",
      "Evaluating step(5): 0.84 across 50 samples, Max potential: 0.84: 100%|██████████| 50/50 [00:16<00:00,  3.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer step: 0.84 > 0.8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 100/100 [00:00<00:00, 1658.72it/s]\n",
      "Evaluating step(4): 0.91 across 100 samples, Max potential: 0.91: 100%|██████████| 100/100 [00:29<00:00,  3.37it/s]\n",
      "Training Step: 6:  38%|███▊      | 5/13 [00:56<02:18, 17.27s/it]\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 207.97it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.86it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 494.99it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 805.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 3\n",
      "Moving batch error size: 1\n",
      "Moving batch acc: 0.75\n",
      "Moving batch correct size: 3\n",
      "Moving batch error size: 1\n",
      "Subset Error size: 1\n",
      "Subset Correct size: 2\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 0.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "Subset loss backward time: 4.081957817077637\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 538.35it/s]\n",
      "Evaluating step(5): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00,  3.13it/s]\n",
      "\n",
      "Proposing:  20%|██        | 1/5 [00:02<00:08,  2.13s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 151.18it/s]\n",
      "Evaluating step(5): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 204.61it/s]\n",
      "\n",
      "Proposing:  40%|████      | 2/5 [00:03<00:04,  1.66s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 698.62it/s]\n",
      "Evaluating step(5): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 571.41it/s]\n",
      "\n",
      "Proposing:  60%|██████    | 3/5 [00:05<00:03,  1.61s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, especially when quantities are specified. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 116.83it/s]\n",
      "Evaluating step(5): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:01<00:00,  2.50it/s]\n",
      "\n",
      "Proposing:  80%|████████  | 4/5 [00:07<00:01,  1.88s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 399.65it/s]\n",
      "Evaluating step(5): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 571.09it/s]\n",
      "\n",
      "Proposing: 100%|██████████| 5/5 [00:08<00:00,  1.69s/it]\n",
      "Training Step: 7:  46%|████▌     | 6/13 [01:09<01:53, 16.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "Done with proposals\n",
      "No proposal can improve the subset and full set, go to next step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 59.06it/s]\n",
      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.63it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 410.78it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 4694.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 7\n",
      "Moving batch error size: 1\n",
      "Moving batch acc: 0.875\n",
      "Moving batch correct size: 7\n",
      "Moving batch error size: 1\n",
      "Subset Error size: 1\n",
      "Subset Correct size: 2\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "Subset loss backward time: 3.0843119621276855\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item individually and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 154.50it/s]\n",
      "Evaluating step(6): 1.0 across 3 samples, Max potential: 1.0: 100%|██████████| 3/3 [00:01<00:00,  1.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass subset check: 1.0 > 0.6666666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 8/8 [00:00<00:00, 279.47it/s]\n",
      "Evaluating step(6): 0.875 across 8 samples, Max potential: 0.875: 100%|██████████| 8/8 [00:01<00:00,  4.43it/s]\n",
      "Proposing:   0%|          | 0/5 [00:04<?, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass full check: 0.875 >= 0.875\n",
      "Done with proposals\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2336.58it/s]\n",
      "Evaluating step(7): 0.84 across 50 samples, Max potential: 0.84: 100%|██████████| 50/50 [00:17<00:00,  2.88it/s]\n",
      "Training Step: 8:  54%|█████▍    | 7/13 [01:37<01:58, 19.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.84 <= 0.84\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 148.75it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.04it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 345.11it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 7550.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 11\n",
      "Moving batch error size: 1\n",
      "Moving batch acc: 0.9166666666666666\n",
      "Moving batch correct size: 11\n",
      "Moving batch error size: 1\n",
      "Subset Error size: 1\n",
      "Subset Correct size: 2\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "Subset loss backward time: 2.337067127227783\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item individually, especially when quantities are specified. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 193.84it/s]\n",
      "Evaluating step(7): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:01<00:00,  2.16it/s]\n",
      "\n",
      "Proposing:  20%|██        | 1/5 [00:02<00:09,  2.39s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item individually, especially when quantities are specified in words. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 147.89it/s]\n",
      "Evaluating step(7): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:01<00:00,  2.04it/s]\n",
      "\n",
      "Proposing:  40%|████      | 2/5 [00:04<00:07,  2.41s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item individually, especially when quantities are specified. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 423.61it/s]\n",
      "Evaluating step(7): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 556.86it/s]\n",
      "\n",
      "Proposing:  60%|██████    | 3/5 [00:05<00:03,  1.78s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item individually, especially when quantities are specified. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 532.41it/s]\n",
      "Evaluating step(7): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 522.78it/s]\n",
      "\n",
      "Proposing:  80%|████████  | 4/5 [00:06<00:01,  1.44s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item individually, especially when quantities are specified in words. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 284.18it/s]\n",
      "Evaluating step(7): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 160.35it/s]\n",
      "\n",
      "Proposing: 100%|██████████| 5/5 [00:07<00:00,  1.59s/it]\n",
      "Training Step: 9:  62%|██████▏   | 8/13 [01:50<01:27, 17.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "Done with proposals\n",
      "No proposal can improve the subset and full set, go to next step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 87.73it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.62it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 342.85it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 7157.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 14\n",
      "Moving batch error size: 2\n",
      "Moving batch acc: 0.875\n",
      "Moving batch correct size: 14\n",
      "Moving batch error size: 2\n",
      "Subset Error size: 2\n",
      "Subset Correct size: 4\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_0 score to 0.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "Subset loss backward time: 7.823317050933838\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 166.50it/s]\n",
      "Evaluating step(8): 0.8333 across 6 samples, Max potential: 0.8333: 100%|██████████| 6/6 [00:02<00:00,  2.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass subset check: 0.8333333333333334 > 0.6666666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 16/16 [00:00<00:00, 481.75it/s]\n",
      "Evaluating step(8): 0.875 across 16 samples, Max potential: 0.875: 100%|██████████| 16/16 [00:03<00:00,  5.21it/s]\n",
      "Proposing:   0%|          | 0/5 [00:06<?, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass full check: 0.875 >= 0.875\n",
      "Done with proposals\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1112.82it/s]\n",
      "Evaluating step(9): 0.86 across 50 samples, Max potential: 0.86: 100%|██████████| 50/50 [00:16<00:00,  2.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer step: 0.86 > 0.84\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 100/100 [00:00<00:00, 2395.58it/s]\n",
      "Evaluating step(8): 0.87 across 100 samples, Max potential: 0.87: 100%|██████████| 100/100 [00:30<00:00,  3.30it/s]\n",
      "Training Step: 10:  69%|██████▉   | 9/13 [02:52<02:04, 31.23s/it]\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 212.83it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.04it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 655.18it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1241.84it/s]\n",
      "Training Step: 11:  77%|███████▋  | 10/13 [02:55<01:07, 22.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping batch 9 as acc: 1.0\n",
      "No proposal can improve the subset and full set, go to next step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 93.95it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.23it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 757.71it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1320.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 3\n",
      "Moving batch error size: 1\n",
      "Moving batch acc: 0.75\n",
      "Moving batch correct size: 3\n",
      "Moving batch error size: 1\n",
      "Subset Error size: 1\n",
      "Subset Correct size: 2\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_2 score to 0.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "Subset loss backward time: 3.768970012664795\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, ensuring you categorize them correctly. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 125.10it/s]\n",
      "Evaluating step(10): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:01<00:00,  2.77it/s]\n",
      "\n",
      "Proposing:  20%|██        | 1/5 [00:02<00:08,  2.19s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, ensuring you categorize them correctly. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 571.28it/s]\n",
      "Evaluating step(10): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 429.07it/s]\n",
      "\n",
      "Proposing:  40%|████      | 2/5 [00:03<00:04,  1.58s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to categories and quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 111.64it/s]\n",
      "Evaluating step(10): 1.0 across 3 samples, Max potential: 1.0: 100%|██████████| 3/3 [00:01<00:00,  2.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass subset check: 1.0 > 0.6666666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 455.77it/s]\n",
      "Evaluating step(10): 1.0 across 4 samples, Max potential: 1.0: 100%|██████████| 4/4 [00:00<00:00,  5.14it/s]\n",
      "Proposing:  40%|████      | 2/5 [00:06<00:09,  3.17s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass full check: 1.0 >= 0.75\n",
      "Done with proposals\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1732.93it/s]\n",
      "Evaluating step(11): 0.825 across 40 samples, Max potential: 0.86:  80%|████████  | 40/50 [00:18<00:04,  2.21it/s]\n",
      "Training Step: 12:  85%|████████▍ | 11/13 [03:24<00:49, 24.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.8048780487804879 <= 0.86\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 128.86it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.24it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 470.20it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 2608.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 6\n",
      "Moving batch error size: 2\n",
      "Moving batch acc: 0.75\n",
      "Moving batch correct size: 6\n",
      "Moving batch error size: 2\n",
      "Subset Error size: 2\n",
      "Subset Correct size: 4\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 0.0\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "Subset loss backward time: 6.722561836242676\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, ensuring you categorize them correctly. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 265.78it/s]\n",
      "Evaluating step(11): 0.5 across 6 samples, Max potential: 0.5: 100%|██████████| 6/6 [00:01<00:00,  3.58it/s]\n",
      "\n",
      "Proposing:  20%|██        | 1/5 [00:02<00:10,  2.65s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.5 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, ensuring you categorize them correctly. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 396.33it/s]\n",
      "Evaluating step(11): 0.5 across 6 samples, Max potential: 0.5: 100%|██████████| 6/6 [00:00<00:00, 354.51it/s]\n",
      "\n",
      "Proposing:  40%|████      | 2/5 [00:03<00:05,  1.80s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.5 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, ensuring you categorize them correctly. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 789.39it/s]\n",
      "Evaluating step(11): 0.5 across 6 samples, Max potential: 0.5: 100%|██████████| 6/6 [00:00<00:00, 233.79it/s]\n",
      "\n",
      "Proposing:  60%|██████    | 3/5 [00:04<00:02,  1.49s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.5 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each relevant item, excluding any that do not fit the category. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 181.12it/s]\n",
      "Evaluating step(11): 0.6667 across 6 samples, Max potential: 0.6667: 100%|██████████| 6/6 [00:02<00:00,  2.13it/s]\n",
      "\n",
      "Proposing:  80%|████████  | 4/5 [00:08<00:02,  2.44s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each relevant item, excluding any that do not fit the category. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 807.04it/s]\n",
      "Evaluating step(11): 0.6667 across 6 samples, Max potential: 0.6667: 100%|██████████| 6/6 [00:00<00:00, 275.78it/s]\n",
      "\n",
      "Proposing: 100%|██████████| 5/5 [00:10<00:00,  2.01s/it]\n",
      "Training Step: 12:  92%|█████████▏| 12/13 [03:43<00:18, 18.61s/it]\n",
      "Epoch: 100%|██████████| 1/1 [03:43<00:00, 223.37s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "Done with proposals\n",
      "No proposal can improve the subset and full set, go to next step\n",
      "Reached max steps\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Configuring teacher generator for Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "Teacher generator set: Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "), teacher Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Configuring teacher generator for Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "Teacher generator set: Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "), teacher Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Configuring teacher generator for Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    You are the feedback engine in an optimization system.\n",
      "    \n",
      "    Your role: Provide intelligent and creative feedback for the variable enclosed in <VARIABLE></VARIABLE> tags, based on the objective specified in <OBJECTIVE_FUNCTION></OBJECTIVE_FUNCTION> tags.\n",
      "    1. Focus on the downstream OBJECTIVE without proposing new versions of the variable.\n",
      "    2. Feedback examples: \"Since language models have the X failure mode...\", \"Adding X can fix this error because...\", \"Removing X can improve the objective function because...\", \"Changing X to Y would fix the mistake...\"\n",
      "    3. Consider the variable in the context of its peers if provided.\n",
      "    Remember:\n",
      "    Be concise, critical, and direct.\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <CONVERSATION>\n",
      "    {{conversation_sec}}\n",
      "    </CONVERSATION>\n",
      "    {{objective_instruction_sec}}\n",
      "    , prompt_variables: ['objective_instruction_sec', 'conversation_sec']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      ")\n",
      "Teacher generator set: Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    You are the feedback engine in an optimization system.\n",
      "    \n",
      "    Your role: Provide intelligent and creative feedback for the variable enclosed in <VARIABLE></VARIABLE> tags, based on the objective specified in <OBJECTIVE_FUNCTION></OBJECTIVE_FUNCTION> tags.\n",
      "    1. Focus on the downstream OBJECTIVE without proposing new versions of the variable.\n",
      "    2. Feedback examples: \"Since language models have the X failure mode...\", \"Adding X can fix this error because...\", \"Removing X can improve the objective function because...\", \"Changing X to Y would fix the mistake...\"\n",
      "    3. Consider the variable in the context of its peers if provided.\n",
      "    Remember:\n",
      "    Be concise, critical, and direct.\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <CONVERSATION>\n",
      "    {{conversation_sec}}\n",
      "    </CONVERSATION>\n",
      "    {{objective_instruction_sec}}\n",
      "    , prompt_variables: ['objective_instruction_sec', 'conversation_sec']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "), teacher Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    You are the feedback engine in an optimization system.\n",
      "    \n",
      "    Your role: Provide intelligent and creative feedback for the variable enclosed in <VARIABLE></VARIABLE> tags, based on the objective specified in <OBJECTIVE_FUNCTION></OBJECTIVE_FUNCTION> tags.\n",
      "    1. Focus on the downstream OBJECTIVE without proposing new versions of the variable.\n",
      "    2. Feedback examples: \"Since language models have the X failure mode...\", \"Adding X can fix this error because...\", \"Removing X can improve the objective function because...\", \"Changing X to Y would fix the mistake...\"\n",
      "    3. Consider the variable in the context of its peers if provided.\n",
      "    Remember:\n",
      "    Be concise, critical, and direct.\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <CONVERSATION>\n",
      "    {{conversation_sec}}\n",
      "    </CONVERSATION>\n",
      "    {{objective_instruction_sec}}\n",
      "    , prompt_variables: ['objective_instruction_sec', 'conversation_sec']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      ")\n",
      "Teacher generator configured.\n",
      "save to /root/.adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n",
      "Starting step: 12\n",
      "trainer_results: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Step: 13:   0%|          | 0/12 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 158.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 6c34d6e5-0e3d-4243-834e-fd6c5883f467 already exists. Updating the trace.Trace with id 234e39df-1bc4-41df-a515-895cb2614a53 already exists. Updating the trace.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 8895d6bd-eab0-48af-ad4b-51f8007258b1 already exists. Updating the trace.\n",
      "Trace with id c42fea48-1b90-4388-92c4-b65b4356a3a2 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 490.46it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1656.19it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 247.40it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.77it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 365.97it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 9294.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['c42fea48-1b90-4388-92c4-b65b4356a3a2']\n",
      "New prompts: [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Trombone: 1\\n\\n  2. Violin: 1\\n\\n  3. Clarinet: 1\\n\\n  4. Accordion: 1\\n\\n  5. Flutes: 4\\n\\n  6. Trumpet: 1\\n\\n  7. Drums: 2\\n\\n  8. Piano: 1\\n\\n\\n  Now, let''s add them up:\\n\\n\\n  1 + 1 + 1 + 1 + 4 + 1 + 2 + 1 = 12\\n\\n\\n  Answer: 12'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2578.13it/s]\n",
      "Evaluating step(13): 0.7812 across 32 samples, Max potential: 0.86:  64%|██████▍   | 32/50 [00:23<00:13,  1.35it/s]\n",
      "Training Step: 14:  17%|█▋        | 2/12 [00:27<04:35, 27.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.7575757575757576 <= 0.86, revert\n",
      "Training Step: 14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 136.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id fd34672a-ffd1-498e-a88f-283aa9d4f65d already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 46a8994f-fce6-4031-b251-1c8af31d88d2 already exists. Updating the trace.\n",
      "Trace with id 2bc992c0-9832-47f1-87c3-9f6e4b18ee99 already exists. Updating the trace.\n",
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     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them up:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1784.60it/s]\n",
      "Evaluating step(14): 0.7812 across 32 samples, Max potential: 0.86:  64%|██████▍   | 32/50 [00:19<00:11,  1.61it/s]\n",
      "Training Step: 15:  33%|███▎      | 4/12 [00:52<02:10, 16.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.7575757575757576 <= 0.86, revert\n",
      "Training Step: 15\n"
     ]
    },
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      "Trace with id 660c5004-35d2-4a6d-9a06-1e0b3f032f21 already exists. Updating the trace.\n"
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them up:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2982.93it/s]\n",
      "Evaluating step(15): 0.7812 across 32 samples, Max potential: 0.86:  64%|██████▍   | 32/50 [00:00<00:00, 495.97it/s]\n",
      "Training Step: 16:  42%|████▏     | 5/12 [00:56<01:03,  9.03s/it]"
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     "text": [
      "Fail validation: 0.7575757575757576 <= 0.86, revert\n",
      "Training Step: 16\n"
     ]
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      "Trace with id 0e8910c8-703d-4766-a483-c5691125fd03 already exists. Updating the trace.Trace with id ffe67a7b-7b81-4302-b6ed-4b506570274b already exists. Updating the trace.\n",
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     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them up:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 3902.04it/s]\n",
      "Evaluating step(16): 0.7812 across 32 samples, Max potential: 0.86:  64%|██████▍   | 32/50 [00:00<00:00, 398.91it/s]\n",
      "Training Step: 17:  58%|█████▊    | 7/12 [00:58<00:35,  7.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.7575757575757576 <= 0.86, revert\n",
      "Training Step: 17\n"
     ]
    },
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      "Trace with id d46e538c-832d-4eb5-ba9b-a308f666baba already exists. Updating the trace.\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them up:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2888.08it/s]\n",
      "Evaluating step(17): 0.7812 across 32 samples, Max potential: 0.86:  64%|██████▍   | 32/50 [00:00<00:00, 221.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.7575757575757576 <= 0.86, revert\n"
     ]
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    {
     "name": "stderr",
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      "Training Step: 18:  67%|██████▋   | 8/12 [01:02<00:19,  4.87s/it]"
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     "text": [
      "Training Step: 18\n"
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      "Trace with id d4194dd1-739a-4509-8ac8-7c3f89649ee7 already exists. Updating the trace.Trace with id 1eb770ed-ff6f-481e-8c16-b9749a44a1a6 already exists. Updating the trace.\n",
      "Trace with id 7694df14-3a24-40bd-a3fa-036c2645eca3 already exists. Updating the trace.\n",
      "\n",
      "Trace with id 4cd9f4ec-2648-4e85-8e17-3dae1b8558d3 already exists. Updating the trace.\n"
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     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them up:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2422.27it/s]\n",
      "Evaluating step(18): 0.7812 across 32 samples, Max potential: 0.86:  64%|██████▍   | 32/50 [00:00<00:00, 456.47it/s]\n",
      "Training Step: 19:  75%|███████▌  | 9/12 [01:05<00:13,  4.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.7575757575757576 <= 0.86, revert\n",
      "Training Step: 19\n"
     ]
    },
    {
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      "Trace with id 5124e2e6-2aac-4dd3-ab63-9277a7b806a7 already exists. Updating the trace.\n",
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     ]
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     "name": "stdout",
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      "Trace with id 3a9a47c8-a210-43a4-8d24-b9159babb6e4 already exists. Updating the trace.Trace with id 6c0d3a9a-bb01-4fb3-a68b-1edf66861235 already exists. Updating the trace.\n",
      "\n"
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     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them up:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 3644.75it/s]\n",
      "Evaluating step(19): 0.7812 across 32 samples, Max potential: 0.86:  64%|██████▍   | 32/50 [00:00<00:00, 275.17it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.7575757575757576 <= 0.86, revert\n"
     ]
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    {
     "name": "stderr",
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      "Training Step: 20:  92%|█████████▏| 11/12 [01:09<00:04,  4.32s/it]"
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     "output_type": "stream",
     "text": [
      "Training Step: 20\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id b538075d-01af-4b76-b835-9005f3044609 already exists. Updating the trace.\n",
      "Trace with id dd9d8748-4926-4bcd-902d-6a4c5cb38267 already exists. Updating the trace.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\rTraining:   0%|          | 0/4 [00:00<?, ?it/s]"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Trace with id 60866bed-8020-4610-a39a-a4a730c035db already exists. Updating the trace.\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
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      "Training: 100%|██████████| 4/4 [00:00<00:00,  4.20it/s]\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
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      "Trace with id 85d63f78-39c0-4753-a9fc-52202df48673 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 328.35it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 999.36it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 239.24it/s]\n",
      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.87it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 353.26it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 391.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['60866bed-8020-4610-a39a-a4a730c035db']\n",
      "New prompts: [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Bed: 1\\n\\n  2. Fridge: 1\\n\\n  3. Lamp: 1\\n\\n  4. Toaster: 1\\n\\n  5. Chairs: 4\\n\\n  6. Table: 1\\n\\n\\n  Now, add them all together:\\n\\n\\n  1 + 1 + 1 + 1 + 4 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1763.23it/s]\n",
      "Evaluating step(20): 0.7083 across 24 samples, Max potential: 0.86:  48%|████▊     | 24/50 [00:17<00:18,  1.38it/s]\n",
      "Training Step: 21: 100%|██████████| 12/12 [01:34<00:00,  7.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.68 <= 0.86, revert\n",
      "Training Step: 21\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 208.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id aefd17e5-9682-4420-a820-c484a63d6dcd already exists. Updating the trace.\n",
      "Trace with id 04e77795-cc9b-4530-a883-5f775e3fbc76 already exists. Updating the trace.\n",
      "Trace with id 701be0ee-29e0-42f5-be04-72d2b73e3968 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 4/4 [00:00<00:00,  4.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 433650a5-ca75-4867-b235-3af4a7c55c67 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 187.26it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 2595.49it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 129.91it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.86it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 172.30it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 689.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them up:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 3183.48it/s]\n",
      "Evaluating step(21): 0.7812 across 32 samples, Max potential: 0.86:  64%|██████▍   | 32/50 [00:00<00:00, 438.75it/s]\n",
      "Training Step: 22: : 13it [01:38,  6.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.7575757575757576 <= 0.86, revert\n",
      "Training Step: 22\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data:   0%|          | 0/4 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 4dad0f65-d624-48c2-a795-596c00b0535a already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 86.81it/s]\n",
      "Training:   0%|          | 0/4 [00:00<?, ?it/s]"
     ]
    },
    {
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     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 82cf82ff-d826-4bb1-847c-9938aeec8ff5 already exists. Updating the trace.\n",
      "Trace with id ac43f3d4-d67d-4912-95d6-0baa09b52d9a already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 143.58it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 842.95it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 133.83it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.36it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 326.14it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 307.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['60866bed-8020-4610-a39a-a4a730c035db']\n",
      "New prompts: [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Bed: 1\\n\\n  2. Fridge: 1\\n\\n  3. Lamp: 1\\n\\n  4. Toaster: 1\\n\\n  5. Chairs: 4\\n\\n  6. Table: 1\\n\\n\\n  Now, add them all together:\\n\\n\\n  1 + 1 + 1 + 1 + 4 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 5440.79it/s]\n",
      "Evaluating step(22): 0.7083 across 24 samples, Max potential: 0.86:  48%|████▊     | 24/50 [00:00<00:00, 303.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.68 <= 0.86, revert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 23: : 14it [01:42,  6.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 23\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 91.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id daa5804f-1aad-4f01-b26c-6b31c57f065f already exists. Updating the trace.\n",
      "Trace with id e2bfbbe0-fb79-4df5-9a7d-50c9085947bc already exists. Updating the trace.\n",
      "Trace with id 71d549d2-9cc8-46ba-a7f6-d07f69263fd3 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 840d9ed5-8222-45a9-a406-7445feae9733 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 63.89it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 201.47it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 90.61it/s]\n",
      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.96it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 287.69it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1938.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them up:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 3010.90it/s]\n",
      "Evaluating step(23): 0.7812 across 32 samples, Max potential: 0.86:  64%|██████▍   | 32/50 [00:00<00:00, 423.98it/s]\n",
      "Training Step: 24: : 16it [01:48,  6.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.7575757575757576 <= 0.86, revert\n",
      "Training Step: 24\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 122.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 96c716a1-e984-4fe3-9ce0-e156ac709edb already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\rTraining:   0%|          | 0/4 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 000a3738-1f09-40b0-9f8b-2dec63a3f7f8 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id d71ad721-d21d-42f1-af9b-719ff026406b already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 106.06it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1513.37it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 265.42it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.07it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 171.27it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 862.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['840d9ed5-8222-45a9-a406-7445feae9733']\n",
      "New prompts: [PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item, paying special attention to quantities mentioned. Verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='2f948c14-7f8f-4f46-9e23-d30598d3f47b', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Table: 1\\n\\n  3. Fridge: 1\\n\\n  4. Stove: 1\\n\\n  5. Oven: 1\\n\\n  6. Toaster: 1\\n\\n  7. Couch: 1\\n\\n  8. Cars: 4\\n\\n\\n  Now, add them up:\\n\\n\\n  1 + 1 + 1 + 1 + 1 + 1 + 1 + 4 = 11\\n\\n\\n  Answer: 11'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1210.01it/s]\n",
      "Evaluating step(24): 0.86 across 50 samples, Max potential: 0.86: 100%|██████████| 50/50 [00:18<00:00,  2.69it/s]\n",
      "Training Step: 24: 100%|██████████| 12/12 [02:15<00:00, 11.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.86 <= 0.86, revert\n",
      "Saved ckpt to /root/.adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n",
      "Training time: 359.32386112213135s\n",
      "ckpt_file: /root/.adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train(\n",
    "    debug=False,\n",
    "    max_steps=12,\n",
    "    strategy=\"constrained\",\n",
    "    raw_shots=0,\n",
    "    bootstrap_shots=1,\n",
    "    exclude_input_fields_from_bootstrap_demos=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KAyFhzrG_J4l"
   },
   "source": [
    "Here is our scores for each step:\n",
    "\n",
    "\"val_scores\": [\n",
    "        0.8,\n",
    "        0.8,\n",
    "        0.8,\n",
    "        0.8,\n",
    "        0.8,\n",
    "        0.84,\n",
    "        0.84,\n",
    "        0.84,\n",
    "        0.84,\n",
    "        0.86,\n",
    "        0.86,\n",
    "        0.86,\n",
    "        0.86,\n",
    "        0.86,\n",
    "        0.86,\n",
    "        0.86,\n",
    "        0.86,\n",
    "        0.86,\n",
    "        0.86,\n",
    "        0.86,\n",
    "        0.86,\n",
    "        0.86,\n",
    "        0.86,\n",
    "        0.86,\n",
    "        0.86\n",
    "    ]\n",
    "\n",
    "  \"test_scores\": [\n",
    "        0.83,\n",
    "        0.83,\n",
    "        0.83,\n",
    "        0.83,\n",
    "        0.83,\n",
    "        0.91,\n",
    "        0.91,\n",
    "        0.91,\n",
    "        0.91,\n",
    "        0.87,\n",
    "        0.87,\n",
    "        0.87,\n",
    "        0.87,\n",
    "        0.87,\n",
    "        0.87,\n",
    "        0.87,\n",
    "        0.87,\n",
    "        0.87,\n",
    "        0.87,\n",
    "        0.87,\n",
    "        0.87,\n",
    "        0.87,\n",
    "        0.87,\n",
    "        0.87,\n",
    "        0.87\n",
    "    ]\n",
    "\n",
    "\n",
    "It is normal when the score of the validation does not exactly match to that of the test set. You can also train with just the test set. You can modify the fit arguments as\n",
    "\n",
    "```\n",
    "trainer.fit(\n",
    "        train_dataset=train_dataset,\n",
    "        val_dataset=test_dataset,\n",
    "        # test_dataset=test_dataset,\n",
    "        debug=debug,\n",
    "        resume_from_ckpt=resume_from_ckpt,\n",
    "    )\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "953BV81y0JFv"
   },
   "source": [
    "# 🔥 Resume Checkpoint\n",
    "\n",
    "We might want to continue from the earlier step and to train more steps\n",
    "\n",
    "This is easy to do.\n",
    "\n",
    "**Note: Ensure you copy the path you had, and replace it, as your run might create a different file name.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kde1V1AE7Ty0",
    "outputId": "52d69b69-0a3a-4780-ca26-25956cc023c7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:adalflow.core.generator:Error copying the prompt_kwargs: 'prompt' is not a valid ParameterType\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-3.5-turbo.db\n",
      "ObjectCountAdalComponent(\n",
      "  eval_fn: compute_single_item, backward_engine: None, backward_engine_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, teacher_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, text_optimizer_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}\n",
      "  (task): ObjectCountTaskPipeline(\n",
      "    (llm_counter): Generator(\n",
      "      model_kwargs={'model': 'gpt-3.5-turbo', 'max_tokens': 2000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "      (prompt): Prompt(\n",
      "        template: <START_OF_SYSTEM_PROMPT>\n",
      "        {{system_prompt}}\n",
      "        {# Few shot demos #}\n",
      "        {% if few_shot_demos is not none %}\n",
      "        Here are some examples:\n",
      "        {{few_shot_demos}}\n",
      "        {% endif %}\n",
      "        <END_OF_SYSTEM_PROMPT>\n",
      "        <START_OF_USER>\n",
      "        {{input_str}}\n",
      "        <END_OF_USER>\n",
      "        , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': None}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "      )\n",
      "      (model_client): OpenAIClient()\n",
      "      (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "    )\n",
      "  )\n",
      "  (loss_fn): EvalFnToTextLoss()\n",
      ")\n",
      "Trainer(\n",
      "  (adaltask): ObjectCountAdalComponent(\n",
      "    eval_fn: compute_single_item, backward_engine: None, backward_engine_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, teacher_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, text_optimizer_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}\n",
      "    (task): ObjectCountTaskPipeline(\n",
      "      (llm_counter): Generator(\n",
      "        model_kwargs={'model': 'gpt-3.5-turbo', 'max_tokens': 2000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "        (prompt): Prompt(\n",
      "          template: <START_OF_SYSTEM_PROMPT>\n",
      "          {{system_prompt}}\n",
      "          {# Few shot demos #}\n",
      "          {% if few_shot_demos is not none %}\n",
      "          Here are some examples:\n",
      "          {{few_shot_demos}}\n",
      "          {% endif %}\n",
      "          <END_OF_SYSTEM_PROMPT>\n",
      "          <START_OF_USER>\n",
      "          {{input_str}}\n",
      "          <END_OF_USER>\n",
      "          , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': None}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "        )\n",
      "        (model_client): OpenAIClient()\n",
      "        (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "      )\n",
      "    )\n",
      "    (loss_fn): EvalFnToTextLoss()\n",
      "  )\n",
      ")\n",
      "raw_shots: 0, bootstrap_shots: 1\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Configuring teacher generator for Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "Teacher generator set: Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "), teacher Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "Teacher generator configured.\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Backward engine configured for all generators.\n",
      "Restoring prompts: PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True)\n",
      "save to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Step: 27:   0%|          | 0/13 [00:00<?, ?it/s]\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 417.64it/s]\n",
      "Training: 100%|██████████| 4/4 [00:00<00:00, 1073.40it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 571.14it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1126.21it/s]\n",
      "Training Step: 28:   0%|          | 0/13 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping batch 0 as acc: 1.0\n",
      "No proposal can improve the subset and full set, go to next step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 604.56it/s]\n",
      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.83it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 540.00it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1445.81it/s]\n",
      "Training Step: 29:  15%|█▌        | 2/13 [00:02<00:12,  1.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping batch 1 as acc: 1.0\n",
      "No proposal can improve the subset and full set, go to next step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 318.87it/s]\n",
      "Training: 100%|██████████| 4/4 [00:00<00:00,  4.06it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 458.88it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1186.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 3\n",
      "Moving batch error size: 1\n",
      "Moving batch acc: 0.75\n",
      "Moving batch correct size: 3\n",
      "Moving batch error size: 1\n",
      "Subset Error size: 1\n",
      "Subset Correct size: 2\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "setting pred name Generator_outputy_pred_2 score to 0.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "Subset loss backward time: 4.518843650817871\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 142.52it/s]\n",
      "Evaluating step(2): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:01<00:00,  1.56it/s]\n",
      "\n",
      "Proposing:  20%|██        | 1/5 [00:02<00:11,  2.99s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities specified in the input. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 118.95it/s]\n",
      "Evaluating step(2): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:01<00:00,  1.76it/s]\n",
      "\n",
      "Proposing:  40%|████      | 2/5 [00:05<00:08,  2.85s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 129.26it/s]\n",
      "Evaluating step(2): 1.0 across 3 samples, Max potential: 1.0: 100%|██████████| 3/3 [00:01<00:00,  1.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass subset check: 1.0 > 0.6666666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 263.51it/s]\n",
      "Evaluating step(2): 1.0 across 4 samples, Max potential: 1.0: 100%|██████████| 4/4 [00:00<00:00,  4.20it/s]\n",
      "Proposing:  40%|████      | 2/5 [00:10<00:15,  5.11s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass full check: 1.0 >= 0.75\n",
      "Done with proposals\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2287.37it/s]\n",
      "Evaluating step(29): 0.8158 across 38 samples, Max potential: 0.86:  76%|███████▌  | 38/50 [00:17<00:05,  2.17it/s]\n",
      "Training Step: 30:  23%|██▎       | 3/13 [00:35<02:25, 14.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.7948717948717948 <= 0.86\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 268.93it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.69it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 603.76it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 8825.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 7\n",
      "Moving batch error size: 1\n",
      "Moving batch acc: 0.875\n",
      "Moving batch correct size: 7\n",
      "Moving batch error size: 1\n",
      "Subset Error size: 1\n",
      "Subset Correct size: 2\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "Subset loss backward time: 2.2182435989379883\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly and ensure you account for all items. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 160.12it/s]\n",
      "Evaluating step(3): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:01<00:00,  1.72it/s]\n",
      "\n",
      "Proposing:  20%|██        | 1/5 [00:02<00:11,  2.83s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly and ensure each item is counted correctly. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 89.23it/s]\n",
      "Evaluating step(3): 1.0 across 3 samples, Max potential: 1.0: 100%|██████████| 3/3 [00:01<00:00,  1.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass subset check: 1.0 > 0.6666666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 8/8 [00:00<00:00, 281.73it/s]\n",
      "Evaluating step(3): 1.0 across 8 samples, Max potential: 1.0: 100%|██████████| 8/8 [00:02<00:00,  2.96it/s]\n",
      "Proposing:  20%|██        | 1/5 [00:08<00:34,  8.54s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass full check: 1.0 >= 0.875\n",
      "Done with proposals\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1910.10it/s]\n",
      "Evaluating step(30): 0.72 across 25 samples, Max potential: 0.86:  50%|█████     | 25/50 [00:18<00:18,  1.38it/s]\n",
      "Training Step: 31:  31%|███       | 4/13 [01:05<03:03, 20.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.6923076923076923 <= 0.86\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 310.31it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.75it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 454.32it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 12336.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 11\n",
      "Moving batch error size: 1\n",
      "Moving batch acc: 0.9166666666666666\n",
      "Moving batch correct size: 11\n",
      "Moving batch error size: 1\n",
      "Subset Error size: 1\n",
      "Subset Correct size: 2\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "Subset loss backward time: 2.028568983078003\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly and ensure each item is counted correctly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 121.52it/s]\n",
      "Evaluating step(4): 1.0 across 3 samples, Max potential: 1.0: 100%|██████████| 3/3 [00:01<00:00,  2.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass subset check: 1.0 > 0.6666666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 12/12 [00:00<00:00, 724.90it/s]\n",
      "Evaluating step(4): 1.0 across 12 samples, Max potential: 1.0: 100%|██████████| 12/12 [00:03<00:00,  3.66it/s]\n",
      "Proposing:   0%|          | 0/5 [00:05<?, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass full check: 1.0 >= 0.9166666666666666\n",
      "Done with proposals\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2233.56it/s]\n",
      "Evaluating step(31): 0.8511 across 47 samples, Max potential: 0.86:  94%|█████████▍| 47/50 [00:16<00:01,  2.81it/s]\n",
      "Training Step: 32:  38%|███▊      | 5/13 [01:31<02:58, 22.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.8333333333333334 <= 0.86\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 269.31it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.20it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 606.49it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1212.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 15\n",
      "Moving batch error size: 1\n",
      "Moving batch acc: 0.9375\n",
      "Moving batch correct size: 15\n",
      "Moving batch error size: 1\n",
      "Subset Error size: 1\n",
      "Subset Correct size: 2\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "Subset loss backward time: 3.2150633335113525\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 130.57it/s]\n",
      "Evaluating step(5): 0.5 across 2 samples, Max potential: 0.6667:  33%|███▎      | 1/3 [00:01<00:02,  1.39s/it]INFO:backoff:Backing off call(...) for 0.2s (openai.InternalServerError: <html>\n",
      "<head><title>500 Internal Server Error</title></head>\n",
      "<body>\n",
      "<center><h1>500 Internal Server Error</h1></center>\n",
      "<hr><center>nginx</center>\n",
      "</body>\n",
      "</html>)\n",
      "Evaluating step(5): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:50<00:00, 16.89s/it]\n",
      "\n",
      "Proposing:  20%|██        | 1/5 [00:52<03:28, 52.11s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 645.05it/s]\n",
      "Evaluating step(5): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 298.94it/s]\n",
      "\n",
      "Proposing:  40%|████      | 2/5 [00:53<01:07, 22.46s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 751.40it/s]\n",
      "Evaluating step(5): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 360.88it/s]\n",
      "\n",
      "Proposing:  60%|██████    | 3/5 [00:54<00:25, 12.66s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 332.13it/s]\n",
      "Evaluating step(5): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 276.08it/s]\n",
      "\n",
      "Proposing:  80%|████████  | 4/5 [00:55<00:08,  8.12s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 440.13it/s]\n",
      "Evaluating step(5): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 235.96it/s]\n",
      "\n",
      "Proposing: 100%|██████████| 5/5 [00:57<00:00, 11.41s/it]\n",
      "Training Step: 33:  46%|████▌     | 6/13 [02:33<04:07, 35.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "Done with proposals\n",
      "No proposal can improve the subset and full set, go to next step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 317.05it/s]\n",
      "Training: 100%|██████████| 4/4 [00:00<00:00, 676.47it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 543.36it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1518.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 18\n",
      "Moving batch error size: 2\n",
      "Moving batch acc: 0.9\n",
      "Moving batch correct size: 18\n",
      "Moving batch error size: 2\n",
      "Subset Error size: 2\n",
      "Subset Correct size: 4\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "setting pred name Generator_outputy_pred_2 score to 0.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "Subset loss backward time: 7.857504606246948\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 282.66it/s]\n",
      "Evaluating step(6): 0.5 across 6 samples, Max potential: 0.5: 100%|██████████| 6/6 [00:02<00:00,  2.75it/s]\n",
      "\n",
      "Proposing:  20%|██        | 1/5 [00:03<00:13,  3.26s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.5 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 687.22it/s]\n",
      "Evaluating step(6): 0.5 across 6 samples, Max potential: 0.5: 100%|██████████| 6/6 [00:00<00:00, 539.26it/s]\n",
      "\n",
      "Proposing:  40%|████      | 2/5 [00:04<00:06,  2.16s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.5 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 608.62it/s]\n",
      "Evaluating step(6): 0.5 across 6 samples, Max potential: 0.5: 100%|██████████| 6/6 [00:00<00:00, 246.48it/s]\n",
      "\n",
      "Proposing:  60%|██████    | 3/5 [00:05<00:03,  1.68s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.5 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 417.60it/s]\n",
      "Evaluating step(6): 0.5 across 6 samples, Max potential: 0.5: 100%|██████████| 6/6 [00:00<00:00, 422.96it/s]\n",
      "\n",
      "Proposing:  80%|████████  | 4/5 [00:07<00:01,  1.58s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.5 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 464.91it/s]\n",
      "Evaluating step(6): 0.5 across 6 samples, Max potential: 0.5: 100%|██████████| 6/6 [00:00<00:00, 269.93it/s]\n",
      "\n",
      "Proposing: 100%|██████████| 5/5 [00:08<00:00,  1.67s/it]\n",
      "Training Step: 34:  54%|█████▍    | 7/13 [02:49<02:55, 29.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.5 <= 0.6666666666666666\n",
      "Done with proposals\n",
      "No proposal can improve the subset and full set, go to next step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 104.68it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.42it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 556.85it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 14230.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 22\n",
      "Moving batch error size: 2\n",
      "Moving batch acc: 0.9\n",
      "Moving batch correct size: 18\n",
      "Moving batch error size: 2\n",
      "Subset Error size: 2\n",
      "Subset Correct size: 4\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "Subset loss backward time: 6.2225048542022705\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 584.16it/s]\n",
      "Evaluating step(7): 0.5 across 6 samples, Max potential: 0.5: 100%|██████████| 6/6 [00:01<00:00,  4.41it/s]\n",
      "\n",
      "Proposing:  20%|██        | 1/5 [00:02<00:10,  2.54s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.5 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 943.25it/s]\n",
      "Evaluating step(7): 0.5 across 6 samples, Max potential: 0.5: 100%|██████████| 6/6 [00:00<00:00, 367.37it/s]\n",
      "\n",
      "Proposing:  40%|████      | 2/5 [00:03<00:04,  1.65s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.5 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 802.76it/s]\n",
      "Evaluating step(7): 0.5 across 6 samples, Max potential: 0.5: 100%|██████████| 6/6 [00:00<00:00, 290.57it/s]\n",
      "\n",
      "Proposing:  60%|██████    | 3/5 [00:04<00:02,  1.44s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.5 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 736.81it/s]\n",
      "Evaluating step(7): 0.5 across 6 samples, Max potential: 0.5: 100%|██████████| 6/6 [00:00<00:00, 352.92it/s]\n",
      "\n",
      "Proposing:  80%|████████  | 4/5 [00:05<00:01,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.5 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 596.84it/s]\n",
      "Evaluating step(7): 0.5 across 6 samples, Max potential: 0.5: 100%|██████████| 6/6 [00:00<00:00, 250.75it/s]\n",
      "\n",
      "Proposing: 100%|██████████| 5/5 [00:07<00:00,  1.45s/it]\n",
      "Training Step: 35:  62%|██████▏   | 8/13 [03:04<02:04, 24.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.5 <= 0.6666666666666666\n",
      "Done with proposals\n",
      "No proposal can improve the subset and full set, go to next step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 70.79it/s]\n",
      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.78it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 388.55it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 2027.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 22\n",
      "Moving batch error size: 2\n",
      "Moving batch acc: 0.9\n",
      "Moving batch correct size: 18\n",
      "Moving batch error size: 2\n",
      "Subset Error size: 2\n",
      "Subset Correct size: 4\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "Subset loss backward time: 5.618266582489014\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 304.00it/s]\n",
      "Evaluating step(8): 0.6667 across 6 samples, Max potential: 0.6667: 100%|██████████| 6/6 [00:02<00:00,  2.79it/s]\n",
      "\n",
      "Proposing:  20%|██        | 1/5 [00:03<00:13,  3.44s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly and ensure each item is counted correctly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 128.97it/s]\n",
      "Evaluating step(8): 1.0 across 6 samples, Max potential: 1.0: 100%|██████████| 6/6 [00:01<00:00,  3.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass subset check: 1.0 > 0.6666666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 20/20 [00:00<00:00, 649.93it/s]\n",
      "Evaluating step(8): 0.95 across 20 samples, Max potential: 0.95: 100%|██████████| 20/20 [00:02<00:00,  8.93it/s]\n",
      "Proposing:  20%|██        | 1/5 [00:08<00:35,  8.79s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass full check: 0.95 >= 0.9\n",
      "Done with proposals\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2667.62it/s]\n",
      "Evaluating step(35): 0.8511 across 47 samples, Max potential: 0.86:  94%|█████████▍| 47/50 [00:00<00:00, 559.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.8333333333333334 <= 0.86\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Step: 36:  69%|██████▉   | 9/13 [03:21<01:29, 22.39s/it]\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 154.85it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.33it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 610.06it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1798.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 22\n",
      "Moving batch error size: 2\n",
      "Moving batch acc: 0.95\n",
      "Moving batch correct size: 19\n",
      "Moving batch error size: 1\n",
      "Subset Error size: 1\n",
      "Subset Correct size: 2\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "Subset loss backward time: 2.553833246231079\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 228.47it/s]\n",
      "Evaluating step(9): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:01<00:00,  2.44it/s]\n",
      "\n",
      "Proposing:  20%|██        | 1/5 [00:02<00:09,  2.47s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 700.57it/s]\n",
      "Evaluating step(9): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 207.56it/s]\n",
      "\n",
      "Proposing:  40%|████      | 2/5 [00:03<00:05,  1.69s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 782.91it/s]\n",
      "Evaluating step(9): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 712.51it/s]\n",
      "\n",
      "Proposing:  60%|██████    | 3/5 [00:04<00:02,  1.49s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 269.05it/s]\n",
      "Evaluating step(9): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 266.32it/s]\n",
      "\n",
      "Proposing:  80%|████████  | 4/5 [00:06<00:01,  1.40s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 466.64it/s]\n",
      "Evaluating step(9): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 498.14it/s]\n",
      "\n",
      "Proposing: 100%|██████████| 5/5 [00:07<00:00,  1.48s/it]\n",
      "Training Step: 37:  77%|███████▋  | 10/13 [03:33<00:56, 18.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "Done with proposals\n",
      "No proposal can improve the subset and full set, go to next step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 115.54it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.77it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 561.81it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1002.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 23\n",
      "Moving batch error size: 1\n",
      "Moving batch acc: 0.95\n",
      "Moving batch correct size: 19\n",
      "Moving batch error size: 1\n",
      "Subset Error size: 1\n",
      "Subset Correct size: 2\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "Subset loss backward time: 2.35148024559021\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 139.22it/s]\n",
      "Evaluating step(10): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00,  3.95it/s]\n",
      "\n",
      "Proposing:  20%|██        | 1/5 [00:01<00:07,  1.81s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 277.60it/s]\n",
      "Evaluating step(10): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 561.39it/s]\n",
      "\n",
      "Proposing:  40%|████      | 2/5 [00:02<00:04,  1.42s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 736.01it/s]\n",
      "Evaluating step(10): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 168.63it/s]\n",
      "\n",
      "Proposing:  60%|██████    | 3/5 [00:03<00:02,  1.24s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 441.77it/s]\n",
      "Evaluating step(10): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 518.09it/s]\n",
      "\n",
      "Proposing:  80%|████████  | 4/5 [00:05<00:01,  1.19s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 396.70it/s]\n",
      "Evaluating step(10): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00, 199.84it/s]\n",
      "\n",
      "Proposing: 100%|██████████| 5/5 [00:06<00:00,  1.27s/it]\n",
      "Training Step: 38:  85%|████████▍ | 11/13 [03:43<00:32, 16.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "Done with proposals\n",
      "No proposal can improve the subset and full set, go to next step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 138.49it/s]\n",
      "Training: 100%|██████████| 4/4 [00:00<00:00,  6.41it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 610.01it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 10665.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moving batch correct size: 22\n",
      "Moving batch error size: 2\n",
      "Moving batch acc: 0.9\n",
      "Moving batch correct size: 18\n",
      "Moving batch error size: 2\n",
      "Subset Error size: 2\n",
      "Subset Correct size: 4\n",
      "Subset score: 0.6666666666666666\n",
      "Subset batch acc: 0.6666666666666666\n",
      "Subset loss backward...\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_2 score to 0.0\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "Subset loss backward time: 11.797855138778687\n",
      "Optimizer propose...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 221.09it/s]\n",
      "Evaluating step(11): 0.6667 across 6 samples, Max potential: 0.6667: 100%|██████████| 6/6 [00:01<00:00,  4.45it/s]\n",
      "\n",
      "Proposing:  20%|██        | 1/5 [00:02<00:09,  2.46s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 690.80it/s]\n",
      "Evaluating step(11): 0.6667 across 6 samples, Max potential: 0.6667: 100%|██████████| 6/6 [00:00<00:00, 309.16it/s]\n",
      "\n",
      "Proposing:  40%|████      | 2/5 [00:03<00:04,  1.61s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 488.13it/s]\n",
      "Evaluating step(11): 0.6667 across 6 samples, Max potential: 0.6667: 100%|██████████| 6/6 [00:00<00:00, 365.81it/s]\n",
      "\n",
      "Proposing:  60%|██████    | 3/5 [00:04<00:02,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 693.52it/s]\n",
      "Evaluating step(11): 0.6667 across 6 samples, Max potential: 0.6667: 100%|██████████| 6/6 [00:00<00:00, 272.61it/s]\n",
      "\n",
      "Proposing:  80%|████████  | 4/5 [00:05<00:01,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "New prompts:  [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Pay special attention to quantities mentioned explicitly, including multiples. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loading Data: 100%|██████████| 6/6 [00:00<00:00, 767.58it/s]\n",
      "Evaluating step(11): 0.6667 across 6 samples, Max potential: 0.6667: 100%|██████████| 6/6 [00:00<00:00, 719.89it/s]\n",
      "\n",
      "Proposing: 100%|██████████| 5/5 [00:07<00:00,  1.41s/it]\n",
      "Training Step: 38:  92%|█████████▏| 12/13 [04:02<00:20, 20.21s/it]\n",
      "Epoch: 100%|██████████| 1/1 [04:02<00:00, 242.58s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
      "Done with proposals\n",
      "No proposal can improve the subset and full set, go to next step\n",
      "Reached max steps\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Configuring teacher generator for Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "Teacher generator set: Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "), teacher Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Configuring teacher generator for Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "Teacher generator set: Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "), teacher Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Configuring teacher generator for Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    You are the feedback engine in an optimization system.\n",
      "    \n",
      "    Your role: Provide intelligent and creative feedback for the variable enclosed in <VARIABLE></VARIABLE> tags, based on the objective specified in <OBJECTIVE_FUNCTION></OBJECTIVE_FUNCTION> tags.\n",
      "    1. Focus on the downstream OBJECTIVE without proposing new versions of the variable.\n",
      "    2. Feedback examples: \"Since language models have the X failure mode...\", \"Adding X can fix this error because...\", \"Removing X can improve the objective function because...\", \"Changing X to Y would fix the mistake...\"\n",
      "    3. Consider the variable in the context of its peers if provided.\n",
      "    Remember:\n",
      "    Be concise, critical, and direct.\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <CONVERSATION>\n",
      "    {{conversation_sec}}\n",
      "    </CONVERSATION>\n",
      "    {{objective_instruction_sec}}\n",
      "    , prompt_variables: ['objective_instruction_sec', 'conversation_sec']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      ")\n",
      "Teacher generator set: Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    You are the feedback engine in an optimization system.\n",
      "    \n",
      "    Your role: Provide intelligent and creative feedback for the variable enclosed in <VARIABLE></VARIABLE> tags, based on the objective specified in <OBJECTIVE_FUNCTION></OBJECTIVE_FUNCTION> tags.\n",
      "    1. Focus on the downstream OBJECTIVE without proposing new versions of the variable.\n",
      "    2. Feedback examples: \"Since language models have the X failure mode...\", \"Adding X can fix this error because...\", \"Removing X can improve the objective function because...\", \"Changing X to Y would fix the mistake...\"\n",
      "    3. Consider the variable in the context of its peers if provided.\n",
      "    Remember:\n",
      "    Be concise, critical, and direct.\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <CONVERSATION>\n",
      "    {{conversation_sec}}\n",
      "    </CONVERSATION>\n",
      "    {{objective_instruction_sec}}\n",
      "    , prompt_variables: ['objective_instruction_sec', 'conversation_sec']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "), teacher Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    You are the feedback engine in an optimization system.\n",
      "    \n",
      "    Your role: Provide intelligent and creative feedback for the variable enclosed in <VARIABLE></VARIABLE> tags, based on the objective specified in <OBJECTIVE_FUNCTION></OBJECTIVE_FUNCTION> tags.\n",
      "    1. Focus on the downstream OBJECTIVE without proposing new versions of the variable.\n",
      "    2. Feedback examples: \"Since language models have the X failure mode...\", \"Adding X can fix this error because...\", \"Removing X can improve the objective function because...\", \"Changing X to Y would fix the mistake...\"\n",
      "    3. Consider the variable in the context of its peers if provided.\n",
      "    Remember:\n",
      "    Be concise, critical, and direct.\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <CONVERSATION>\n",
      "    {{conversation_sec}}\n",
      "    </CONVERSATION>\n",
      "    {{objective_instruction_sec}}\n",
      "    , prompt_variables: ['objective_instruction_sec', 'conversation_sec']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      ")\n",
      "Teacher generator configured.\n",
      "save to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n",
      "Starting step: 38\n",
      "trainer_results: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]\n"
     ]
    },
    {
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      "Training Step: 39:   0%|          | 0/12 [00:00<?, ?it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 39\n"
     ]
    },
    {
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      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 161.31it/s]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 54e272c5-1360-462e-b773-4c58c61472ee already exists. Updating the trace.\n",
      "Trace with id 0e8910c8-703d-4766-a483-c5691125fd03 already exists. Updating the trace.\n",
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      "Trace with id fe9b883c-4f47-44f7-a388-b03a2fb10413 already exists. Updating the trace.\n"
     ]
    },
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     "name": "stderr",
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      "Training: 100%|██████████| 4/4 [00:00<00:00, 812.53it/s]\n",
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['0e8910c8-703d-4766-a483-c5691125fd03']\n",
      "New prompts: [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Fridge\\n\\n  2. Chair\\n\\n  3. Bed\\n\\n  4. Oven\\n\\n  5. Microwave\\n\\n  6. Car\\n\\n\\n  There are 6 objects in total.\\n\\n\\n  Answer: 6'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 3194.64it/s]\n",
      "Evaluating step(39): 0.6818 across 22 samples, Max potential: 0.86:  44%|████▍     | 22/50 [00:15<00:19,  1.45it/s]\n",
      "Training Step: 40:  17%|█▋        | 2/12 [00:17<02:58, 17.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.6521739130434783 <= 0.86, revert\n",
      "Training Step: 40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 697.57it/s]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
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      "Trace with id daa5804f-1aad-4f01-b26c-6b31c57f065f already exists. Updating the trace.\n",
      "Trace with id 71d549d2-9cc8-46ba-a7f6-d07f69263fd3 already exists. Updating the trace.\n"
     ]
    },
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      "Training: 100%|██████████| 4/4 [00:00<00:00, 562.43it/s]\n"
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    {
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      "Trace with id b538075d-01af-4b76-b835-9005f3044609 already exists. Updating the trace.Trace with id fd34672a-ffd1-498e-a88f-283aa9d4f65d already exists. Updating the trace.\n",
      "\n"
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    },
    {
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them all together:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 3973.84it/s]\n",
      "Evaluating step(40): 0.825 across 40 samples, Max potential: 0.86:  80%|████████  | 40/50 [00:00<00:00, 440.54it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.8048780487804879 <= 0.86, revert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 41:  33%|███▎      | 4/12 [00:22<00:49,  6.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 41\n"
     ]
    },
    {
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     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them all together:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 3444.16it/s]\n",
      "Evaluating step(41): 0.825 across 40 samples, Max potential: 0.86:  80%|████████  | 40/50 [00:00<00:00, 318.28it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.8048780487804879 <= 0.86, revert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 42:  42%|████▏     | 5/12 [00:24<00:25,  3.71s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 42\n"
     ]
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them all together:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 4718.96it/s]\n",
      "Evaluating step(42): 0.825 across 40 samples, Max potential: 0.86:  80%|████████  | 40/50 [00:00<00:00, 404.64it/s]\n",
      "Training Step: 43:  58%|█████▊    | 7/12 [00:27<00:17,  3.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.8048780487804879 <= 0.86, revert\n",
      "Training Step: 43\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Trace with id 46a8994f-fce6-4031-b251-1c8af31d88d2 already exists. Updating the trace.Trace with id 3a9a47c8-a210-43a4-8d24-b9159babb6e4 already exists. Updating the trace.\n",
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them all together:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 4960.15it/s]\n",
      "Evaluating step(43): 0.825 across 40 samples, Max potential: 0.86:  80%|████████  | 40/50 [00:00<00:00, 464.52it/s]\n",
      "Training Step: 44:  67%|██████▋   | 8/12 [00:30<00:10,  2.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.8048780487804879 <= 0.86, revert\n",
      "Training Step: 44\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
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      "Trace with id aefd17e5-9682-4420-a820-c484a63d6dcd already exists. Updating the trace.\n",
      "Trace with id 2bc992c0-9832-47f1-87c3-9f6e4b18ee99 already exists. Updating the trace.Trace with id 945f82c7-03d9-4f49-8267-be7abac2bce6 already exists. Updating the trace.\n",
      "Trace with id 12a6ff3d-f54d-4d89-b5f0-1aec30e96398 already exists. Updating the trace.\n"
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['aefd17e5-9682-4420-a820-c484a63d6dcd']\n",
      "New prompts: [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each vegetable step by step:\\n\\n\\n  1. Carrot: 1\\n\\n  2. Onion: 1\\n\\n  3. Stalk of celery: 1\\n\\n  4. Yams: 3\\n\\n  5. Garlic: 1\\n\\n  6. Head of broccoli: 1\\n\\n  7. Potato: 1\\n\\n\\n  Now, let''s add them up:\\n\\n\\n  1 + 1 + 1 + 3 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1197.95it/s]\n",
      "Evaluating step(44): 0.8333 across 42 samples, Max potential: 0.86:  84%|████████▍ | 42/50 [00:22<00:04,  1.87it/s]\n",
      "Training Step: 45:  75%|███████▌  | 9/12 [00:57<00:24,  8.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.813953488372093 <= 0.86, revert\n",
      "Training Step: 45\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     "text": [
      "Trace with id 701be0ee-29e0-42f5-be04-72d2b73e3968 already exists. Updating the trace.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them all together:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2621.44it/s]\n",
      "Evaluating step(45): 0.825 across 40 samples, Max potential: 0.86:  80%|████████  | 40/50 [00:00<00:00, 306.53it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.8048780487804879 <= 0.86, revert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 46:  92%|█████████▏| 11/12 [00:59<00:06,  6.78s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 46\n"
     ]
    },
    {
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      "Trace with id 82cf82ff-d826-4bb1-847c-9938aeec8ff5 already exists. Updating the trace.\n",
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     ]
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['aefd17e5-9682-4420-a820-c484a63d6dcd']\n",
      "New prompts: [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each vegetable step by step:\\n\\n\\n  1. Carrot: 1\\n\\n  2. Onion: 1\\n\\n  3. Stalk of celery: 1\\n\\n  4. Yams: 3\\n\\n  5. Garlic: 1\\n\\n  6. Head of broccoli: 1\\n\\n  7. Potato: 1\\n\\n\\n  Now, let''s add them up:\\n\\n\\n  1 + 1 + 1 + 3 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 4016.92it/s]\n",
      "Evaluating step(46): 0.8333 across 42 samples, Max potential: 0.86:  84%|████████▍ | 42/50 [00:00<00:00, 303.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.813953488372093 <= 0.86, revert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 47: 100%|██████████| 12/12 [01:01<00:00,  4.42s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 47\n"
     ]
    },
    {
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    {
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      "Trace with id 85d63f78-39c0-4753-a9fc-52202df48673 already exists. Updating the trace.Trace with id 74d1bc97-46cd-406d-8c3a-2f999aae1b2f already exists. Updating the trace.\n",
      "\n",
      "Trace with id 60866bed-8020-4610-a39a-a4a730c035db already exists. Updating the trace.\n"
     ]
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them all together:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2836.52it/s]\n",
      "Evaluating step(47): 0.825 across 40 samples, Max potential: 0.86:  80%|████████  | 40/50 [00:00<00:00, 371.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.8048780487804879 <= 0.86, revert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 48: : 13it [01:07,  4.63s/it]"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Training Step: 48\n"
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    {
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      "Trace with id 96c716a1-e984-4fe3-9ce0-e156ac709edb already exists. Updating the trace.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['99607986-e107-46b8-b86b-177b295983c4']\n",
      "New prompts: [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s list and count the vegetables mentioned:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (two cabbages)\\n\\n  4. Garlic\\n\\n  5. Carrot\\n\\n  6. Broccoli (head of broccoli)\\n\\n  7. Potato\\n\\n  8. Celery (stalk of celery)\\n\\n  9. Lettuce (lettuce head)\\n\\n\\n  Now, let''s count each vegetable:\\n\\n\\n  1. Yam: 1\\n\\n  2. Cauliflower: 1\\n\\n  3. Cabbages: 2\\n\\n  4. Garlic: 1\\n\\n  5. Carrot: 1\\n\\n  6. Broccoli: 1\\n\\n  7. Potato: 1\\n\\n  8. Celery: 1\\n\\n  9. Lettuce: 1\\n\\n\\n  Adding them up:\\n\\n\\n  1 + 1 + 2 + 1 + 1 + 1 + 1 + 1 + 1 = 10\\n\\n\\n  Answer: 10'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1430.74it/s]\n",
      "Evaluating step(48): 0.825 across 40 samples, Max potential: 0.86:  80%|████████  | 40/50 [00:28<00:07,  1.41it/s]\n",
      "Training Step: 49: : 14it [01:39, 11.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.8048780487804879 <= 0.86, revert\n",
      "Training Step: 49\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
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     "name": "stdout",
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      "Trace with id ffe67a7b-7b81-4302-b6ed-4b506570274b already exists. Updating the trace.\n",
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      "Trace with id d71ad721-d21d-42f1-af9b-719ff026406b already exists. Updating the trace.Trace with id e250f80e-334e-4f85-ac1f-df9a2013d578 already exists. Updating the trace.\n",
      "\n"
     ]
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     "name": "stderr",
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      "Training: 100%|██████████| 4/4 [00:00<00:00, 421.38it/s]\n",
      "\n",
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them all together:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 3168.14it/s]\n",
      "Evaluating step(49): 0.825 across 40 samples, Max potential: 0.86:  80%|████████  | 40/50 [00:00<00:00, 492.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.8048780487804879 <= 0.86, revert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 50: : 16it [01:42,  9.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id c42fea48-1b90-4388-92c4-b65b4356a3a2 already exists. Updating the trace.\n",
      "Trace with id 660c5004-35d2-4a6d-9a06-1e0b3f032f21 already exists. Updating the trace.\n",
      "Trace with id 7694df14-3a24-40bd-a3fa-036c2645eca3 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 4/4 [00:03<00:00,  1.04it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 220.83it/s]\n",
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      "\n",
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['fd34672a-ffd1-498e-a88f-283aa9d4f65d']\n",
      "New prompts: [PromptData(id='a530c025-f25c-4423-b146-215ff73586f4', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='0b4dc918-1afb-4f03-9193-90ec51a9abab', name='llm_counter.few_shot_demos', data=\"Example: 'Let''s count each item step by step:\\n\\n\\n  1. Microwave: 1\\n\\n  2. Lamp: 1\\n\\n  3. Cars: 4\\n\\n  4. Stove: 1\\n\\n  5. Toaster: 1\\n\\n  6. Bed: 1\\n\\n\\n  Now, add them all together:\\n\\n\\n  1 + 1 + 4 + 1 + 1 + 1 = 9\\n\\n\\n  Answer: 9'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2261.91it/s]\n",
      "Evaluating step(50): 0.825 across 40 samples, Max potential: 0.86:  80%|████████  | 40/50 [00:00<00:00, 281.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.8048780487804879 <= 0.86, revert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 50: 100%|██████████| 12/12 [01:49<00:00,  9.15s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved ckpt to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n",
      "Training time: 352.5873613357544s\n",
      "ckpt_file: /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    }
   ],
   "source": [
    "ckpt_path = \"/content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\"\n",
    "\n",
    "train(\n",
    "    debug=False,\n",
    "    max_steps=12,\n",
    "    strategy=\"constrained\",\n",
    "    raw_shots=0,\n",
    "    bootstrap_shots=1,\n",
    "    resume_from_ckpt=ckpt_path,\n",
    "    exclude_input_fields_from_bootstrap_demos=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m5fZGQqLE78r"
   },
   "source": [
    "I decide to try more, this time, using strategy \"random\". And in the bootstrap demo, there is one shot, but I ensure I also add the \"input\" in the demonstration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "78JAv4ULEn07",
    "outputId": "e87bb360-fc26-4dbd-d163-86ab32c292df"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:adalflow.core.generator:Error copying the prompt_kwargs: 'prompt' is not a valid ParameterType\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-3.5-turbo.db\n",
      "ObjectCountAdalComponent(\n",
      "  eval_fn: compute_single_item, backward_engine: None, backward_engine_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, teacher_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, text_optimizer_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}\n",
      "  (task): ObjectCountTaskPipeline(\n",
      "    (llm_counter): Generator(\n",
      "      model_kwargs={'model': 'gpt-3.5-turbo', 'max_tokens': 2000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "      (prompt): Prompt(\n",
      "        template: <START_OF_SYSTEM_PROMPT>\n",
      "        {{system_prompt}}\n",
      "        {# Few shot demos #}\n",
      "        {% if few_shot_demos is not none %}\n",
      "        Here are some examples:\n",
      "        {{few_shot_demos}}\n",
      "        {% endif %}\n",
      "        <END_OF_SYSTEM_PROMPT>\n",
      "        <START_OF_USER>\n",
      "        {{input_str}}\n",
      "        <END_OF_USER>\n",
      "        , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': None}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "      )\n",
      "      (model_client): OpenAIClient()\n",
      "      (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "    )\n",
      "  )\n",
      "  (loss_fn): EvalFnToTextLoss()\n",
      ")\n",
      "Trainer(\n",
      "  (adaltask): ObjectCountAdalComponent(\n",
      "    eval_fn: compute_single_item, backward_engine: None, backward_engine_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, teacher_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, text_optimizer_model_config: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}\n",
      "    (task): ObjectCountTaskPipeline(\n",
      "      (llm_counter): Generator(\n",
      "        model_kwargs={'model': 'gpt-3.5-turbo', 'max_tokens': 2000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "        (prompt): Prompt(\n",
      "          template: <START_OF_SYSTEM_PROMPT>\n",
      "          {{system_prompt}}\n",
      "          {# Few shot demos #}\n",
      "          {% if few_shot_demos is not none %}\n",
      "          Here are some examples:\n",
      "          {{few_shot_demos}}\n",
      "          {% endif %}\n",
      "          <END_OF_SYSTEM_PROMPT>\n",
      "          <START_OF_USER>\n",
      "          {{input_str}}\n",
      "          <END_OF_USER>\n",
      "          , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': None}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "        )\n",
      "        (model_client): OpenAIClient()\n",
      "        (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "      )\n",
      "    )\n",
      "    (loss_fn): EvalFnToTextLoss()\n",
      "  )\n",
      ")\n",
      "raw_shots: 0, bootstrap_shots: 1\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Configuring teacher generator for Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "Teacher generator set: Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "), teacher Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "Teacher generator configured.\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Backward engine configured for all generators.\n",
      "Restoring prompts: PromptData(id='44f6083f-4cf7-4a9a-bf10-20d218ee4106', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True)\n",
      "save to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training Step: 51:   0%|          | 0/13 [00:00<?, ?it/s]\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 415.27it/s]\n",
      "Training: 100%|██████████| 4/4 [00:00<00:00, 224.54it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 423.57it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 10894.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss backward...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "Optimizer propose...\n",
      "New prompts:  [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2199.38it/s]\n",
      "Evaluating step(51): 0.86 across 50 samples, Max potential: 0.86: 100%|██████████| 50/50 [00:12<00:00,  3.97it/s]\n",
      "Training Step: 52:   8%|▊         | 1/13 [00:18<03:38, 18.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.86 <= 0.86\n",
      "Saving checkpoint to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 402.10it/s]\n",
      "Training: 100%|██████████| 4/4 [00:00<00:00, 785.01it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 842.02it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 6660.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss backward...\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "Optimizer propose...\n",
      "New prompts:  [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1760.33it/s]\n",
      "Evaluating step(52): 0.86 across 50 samples, Max potential: 0.86: 100%|██████████| 50/50 [00:12<00:00,  3.96it/s]\n",
      "Training Step: 53:  15%|█▌        | 2/13 [00:36<03:21, 18.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.86 <= 0.86\n",
      "Saving checkpoint to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 571.26it/s]\n",
      "Training: 100%|██████████| 4/4 [00:00<00:00, 988.41it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 608.29it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1177.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss backward...\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "Optimizer propose...\n",
      "New prompts:  [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2074.29it/s]\n",
      "Evaluating step(53): 0.88 across 50 samples, Max potential: 0.88: 100%|██████████| 50/50 [00:16<00:00,  3.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer step: 0.88 > 0.86\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 100/100 [00:00<00:00, 5848.08it/s]\n",
      "Evaluating step(53): 0.9 across 100 samples, Max potential: 0.9: 100%|██████████| 100/100 [00:30<00:00,  3.32it/s]\n",
      "Training Step: 54:  23%|██▎       | 3/13 [01:28<05:35, 33.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving checkpoint to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 297.78it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.95it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 407.40it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 8952.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss backward...\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "Optimizer propose...\n",
      "New prompts:  [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1274.72it/s]\n",
      "Evaluating step(54): 0.94 across 50 samples, Max potential: 0.94: 100%|██████████| 50/50 [00:16<00:00,  3.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer step: 0.94 > 0.88\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 100/100 [00:00<00:00, 6831.78it/s]\n",
      "Evaluating step(54): 0.91 across 100 samples, Max potential: 0.91: 100%|██████████| 100/100 [00:30<00:00,  3.33it/s]\n",
      "Training Step: 55:  31%|███       | 4/13 [02:21<06:10, 41.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving checkpoint to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 152.84it/s]\n",
      "Training: 100%|██████████| 4/4 [00:03<00:00,  1.28it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 688.86it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1318.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss backward...\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 0.0\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "Optimizer propose...\n",
      "New prompts:  [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data='You will answer a reasoning question. Carefully count each item and verify your total. List each item individually, ensuring each is counted as \"1\" regardless of quantity mentioned. Show your calculations step by step. The last line of your response should be: \\'Answer: $VALUE\\' where VALUE is a numerical value.', requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2011.16it/s]\n",
      "Evaluating step(55): 0.8696 across 23 samples, Max potential: 0.94:  46%|████▌     | 23/50 [00:15<00:17,  1.52it/s]\n",
      "Training Step: 56:  38%|███▊      | 5/13 [02:46<04:43, 35.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.8333333333333334 <= 0.94\n",
      "Saving checkpoint to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 93.66it/s]\n",
      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.75it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 646.55it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 2217.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss backward...\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "Optimizer propose...\n",
      "New prompts:  [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 4572.35it/s]\n",
      "Evaluating step(56): 0.94 across 50 samples, Max potential: 0.94: 100%|██████████| 50/50 [00:00<00:00, 390.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.94 <= 0.94\n",
      "Saving checkpoint to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 57:  46%|████▌     | 6/13 [02:54<03:02, 26.03s/it]\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 145.48it/s]\n",
      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.52it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 375.76it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1437.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss backward...\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 0.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "Optimizer propose...\n",
      "New prompts:  [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. Double-check for any grouped items and count them correctly. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1614.47it/s]\n",
      "Evaluating step(57): 0.7857 across 14 samples, Max potential: 0.94:  28%|██▊       | 14/50 [00:19<00:50,  1.41s/it]\n",
      "Training Step: 58:  54%|█████▍    | 7/13 [03:23<02:42, 27.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.7333333333333333 <= 0.94\n",
      "Saving checkpoint to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 137.96it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.94it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 806.79it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 11522.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss backward...\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "Optimizer propose...\n",
      "New prompts:  [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be formatted as: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 3560.17it/s]\n",
      "Evaluating step(58): 0.88 across 25 samples, Max potential: 0.94:  50%|█████     | 25/50 [00:17<00:17,  1.45it/s]\n",
      "Training Step: 59:  62%|██████▏   | 8/13 [03:47<02:10, 26.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.8461538461538461 <= 0.94\n",
      "Saving checkpoint to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 93.90it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.70it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 552.01it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 5648.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss backward...\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_2 score to 0.0\n",
      "Optimizer propose...\n",
      "New prompts:  [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. Double-check your final count. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1770.11it/s]\n",
      "Evaluating step(59): 0.9286 across 42 samples, Max potential: 0.94:  84%|████████▍ | 42/50 [00:16<00:03,  2.49it/s]\n",
      "Training Step: 60:  69%|██████▉   | 9/13 [04:13<01:43, 26.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.9069767441860465 <= 0.94\n",
      "Saving checkpoint to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 314.86it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.10it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 722.53it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 7940.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss backward...\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "Optimizer propose...\n",
      "New prompts:  [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. Double-check your final count for precision. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 7188.43it/s]\n",
      "Evaluating step(60): 0.8966 across 29 samples, Max potential: 0.94:  58%|█████▊    | 29/50 [00:15<00:11,  1.84it/s]\n",
      "Training Step: 61:  77%|███████▋  | 10/13 [04:35<01:14, 24.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.8666666666666667 <= 0.94\n",
      "Saving checkpoint to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 95.68it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.74it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 587.05it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 12520.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss backward...\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "Optimizer propose...\n",
      "New prompts:  [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. Double-check your final count. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 3036.62it/s]\n",
      "Evaluating step(61): 0.9286 across 42 samples, Max potential: 0.94:  84%|████████▍ | 42/50 [00:00<00:00, 327.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.9069767441860465 <= 0.94\n",
      "Saving checkpoint to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 62:  85%|████████▍ | 11/13 [04:44<00:40, 20.14s/it]\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 136.40it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  3.17it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 417.11it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 14339.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss backward...\n",
      "setting pred name Generator_outputy_pred_2 score to 1.0\n",
      "setting pred name Generator_outputy_pred_0 score to 1.0\n",
      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
      "Optimizer propose...\n",
      "New prompts:  [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. Double-check your final count. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=None, requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 5035.06it/s]\n",
      "Evaluating step(62): 0.9286 across 42 samples, Max potential: 0.94:  84%|████████▍ | 42/50 [00:00<00:00, 327.19it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizer revert: 0.9069767441860465 <= 0.94\n",
      "Saving checkpoint to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 62:  92%|█████████▏| 12/13 [04:51<00:24, 24.28s/it]\n",
      "Epoch:   0%|          | 0/1 [04:51<?, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reached max steps\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Configuring teacher generator for Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "Teacher generator set: Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "), teacher Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Configuring teacher generator for Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "Teacher generator set: Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      "), teacher Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    {{system_prompt}}\n",
      "    {# Few shot demos #}\n",
      "    {% if few_shot_demos is not none %}\n",
      "    Here are some examples:\n",
      "    {{few_shot_demos}}\n",
      "    {% endif %}\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <START_OF_USER>\n",
      "    {{input_str}}\n",
      "    <END_OF_USER>\n",
      "    , prompt_kwargs: {'system_prompt': \"You will answer a reasoning question. Think step by step. The last line of your response should be of the following format: 'Answer: $VALUE' where VALUE is a numerical value.\", 'few_shot_demos': 'None'}, prompt_variables: ['input_str', 'few_shot_demos', 'system_prompt']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "  (output_processors): ParseIntegerAnswerComponent(fun_name=parse_integer_answer)\n",
      ")\n",
      "cache_path: /root/.adalflow/cache_OpenAIClient_gpt-4o.db\n",
      "Configuring teacher generator for Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    You are the feedback engine in an optimization system.\n",
      "    \n",
      "    Your role: Provide intelligent and creative feedback for the variable enclosed in <VARIABLE></VARIABLE> tags, based on the objective specified in <OBJECTIVE_FUNCTION></OBJECTIVE_FUNCTION> tags.\n",
      "    1. Focus on the downstream OBJECTIVE without proposing new versions of the variable.\n",
      "    2. Feedback examples: \"Since language models have the X failure mode...\", \"Adding X can fix this error because...\", \"Removing X can improve the objective function because...\", \"Changing X to Y would fix the mistake...\"\n",
      "    3. Consider the variable in the context of its peers if provided.\n",
      "    Remember:\n",
      "    Be concise, critical, and direct.\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <CONVERSATION>\n",
      "    {{conversation_sec}}\n",
      "    </CONVERSATION>\n",
      "    {{objective_instruction_sec}}\n",
      "    , prompt_variables: ['objective_instruction_sec', 'conversation_sec']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      ")\n",
      "Teacher generator set: Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    You are the feedback engine in an optimization system.\n",
      "    \n",
      "    Your role: Provide intelligent and creative feedback for the variable enclosed in <VARIABLE></VARIABLE> tags, based on the objective specified in <OBJECTIVE_FUNCTION></OBJECTIVE_FUNCTION> tags.\n",
      "    1. Focus on the downstream OBJECTIVE without proposing new versions of the variable.\n",
      "    2. Feedback examples: \"Since language models have the X failure mode...\", \"Adding X can fix this error because...\", \"Removing X can improve the objective function because...\", \"Changing X to Y would fix the mistake...\"\n",
      "    3. Consider the variable in the context of its peers if provided.\n",
      "    Remember:\n",
      "    Be concise, critical, and direct.\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <CONVERSATION>\n",
      "    {{conversation_sec}}\n",
      "    </CONVERSATION>\n",
      "    {{objective_instruction_sec}}\n",
      "    , prompt_variables: ['objective_instruction_sec', 'conversation_sec']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      "), teacher Generator(\n",
      "  model_kwargs={'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}, \n",
      "  (prompt): Prompt(\n",
      "    template: <START_OF_SYSTEM_PROMPT>\n",
      "    You are the feedback engine in an optimization system.\n",
      "    \n",
      "    Your role: Provide intelligent and creative feedback for the variable enclosed in <VARIABLE></VARIABLE> tags, based on the objective specified in <OBJECTIVE_FUNCTION></OBJECTIVE_FUNCTION> tags.\n",
      "    1. Focus on the downstream OBJECTIVE without proposing new versions of the variable.\n",
      "    2. Feedback examples: \"Since language models have the X failure mode...\", \"Adding X can fix this error because...\", \"Removing X can improve the objective function because...\", \"Changing X to Y would fix the mistake...\"\n",
      "    3. Consider the variable in the context of its peers if provided.\n",
      "    Remember:\n",
      "    Be concise, critical, and direct.\n",
      "    <END_OF_SYSTEM_PROMPT>\n",
      "    <CONVERSATION>\n",
      "    {{conversation_sec}}\n",
      "    </CONVERSATION>\n",
      "    {{objective_instruction_sec}}\n",
      "    , prompt_variables: ['objective_instruction_sec', 'conversation_sec']\n",
      "  )\n",
      "  (model_client): OpenAIClient()\n",
      ")\n",
      "Teacher generator configured.\n",
      "save to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n",
      "Starting step: 62\n",
      "trainer_results: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 63\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 175.38it/s]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id fd34672a-ffd1-498e-a88f-283aa9d4f65d already exists. Updating the trace.\n",
      "Trace with id 82cf82ff-d826-4bb1-847c-9938aeec8ff5 already exists. Updating the trace.\n"
     ]
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     "output_type": "stream",
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      "\rTraining:   0%|          | 0/4 [00:00<?, ?it/s]"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 46a8994f-fce6-4031-b251-1c8af31d88d2 already exists. Updating the trace.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 4/4 [00:00<00:00,  4.32it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 445.92it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 9063.87it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 132.51it/s]\n",
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      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 913.05it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1900.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['000a3738-1f09-40b0-9f8b-2dec63a3f7f8']\n",
      "New prompts: [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=\"input_str: I have two heads of broccoli, an apple, a lettuce head, and two onions.\\n  How many vegetables do I have?\\nExample: 'Let''s list each item individually and determine if it is a vegetable:\\n\\n\\n  1. Two heads of broccoli (vegetables)\\n\\n  2. An apple (not a vegetable)\\n\\n  3. A lettuce head (vegetable)\\n\\n  4. Two onions (vegetables)\\n\\n\\n  Now, let''s count the vegetables:\\n\\n\\n  1. Two heads of broccoli\\n\\n  2. One lettuce head\\n\\n  3. Two onions\\n\\n\\n  Total number of vegetables:\\n\\n  2 (broccoli) + 1 (lettuce) + 2 (onions) = 5\\n\\n\\n  Answer: 5'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2838.94it/s]\n",
      "Evaluating step(63): 0.5 across 6 samples, Max potential: 0.94:  12%|█▏        | 6/50 [00:31<03:48,  5.20s/it]\n",
      "Training Step: 64:  17%|█▋        | 2/12 [00:36<06:01, 36.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.42857142857142855 <= 0.94, revert\n",
      "Training Step: 64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 60866bed-8020-4610-a39a-a4a730c035db already exists. Updating the trace.\n",
      "Trace with id 7694df14-3a24-40bd-a3fa-036c2645eca3 already exists. Updating the trace."
     ]
    },
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     "output_type": "stream",
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      "\rTraining:   0%|          | 0/4 [00:00<?, ?it/s]"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Trace with id 3835ee47-6951-49ec-b285-621fc1085024 already exists. Updating the trace.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 4/4 [00:00<00:00,  4.64it/s]\n"
     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
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      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 3232.60it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 151.65it/s]\n",
      "Training: 100%|██████████| 4/4 [00:03<00:00,  1.21it/s]\n",
      "\n",
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      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 10845.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['000a3738-1f09-40b0-9f8b-2dec63a3f7f8']\n",
      "New prompts: [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=\"input_str: I have two heads of broccoli, an apple, a lettuce head, and two onions.\\n  How many vegetables do I have?\\nExample: 'Let''s list each item individually and determine if it is a vegetable:\\n\\n\\n  1. Two heads of broccoli (vegetables)\\n\\n  2. An apple (not a vegetable)\\n\\n  3. A lettuce head (vegetable)\\n\\n  4. Two onions (vegetables)\\n\\n\\n  Now, let''s count the vegetables:\\n\\n\\n  1. Two heads of broccoli\\n\\n  2. One lettuce head\\n\\n  3. Two onions\\n\\n\\n  Total number of vegetables:\\n\\n  2 (broccoli) + 1 (lettuce) + 2 (onions) = 5\\n\\n\\n  Answer: 5'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2971.02it/s]\n",
      "Evaluating step(64): 0.5 across 6 samples, Max potential: 0.94:  12%|█▏        | 6/50 [00:00<00:00, 136.83it/s]\n",
      "Training Step: 65:  33%|███▎      | 4/12 [00:41<01:29, 11.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.42857142857142855 <= 0.94, revert\n",
      "Training Step: 65\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 201.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 99607986-e107-46b8-b86b-177b295983c4 already exists. Updating the trace.\n"
     ]
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     "output_type": "stream",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Trace with id ffe67a7b-7b81-4302-b6ed-4b506570274b already exists. Updating the trace.\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\rTraining:  50%|█████     | 2/4 [00:00<00:00,  2.54it/s]"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Trace with id 71d549d2-9cc8-46ba-a7f6-d07f69263fd3 already exists. Updating the trace.\n"
     ]
    },
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     ]
    },
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    },
    {
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     "output_type": "stream",
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      "\n",
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      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 6304.85it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 218.71it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.19it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 858.52it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 768.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['99607986-e107-46b8-b86b-177b295983c4']\n",
      "New prompts: [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=\"input_str: I have a yam, a cauliflower, a bed, two cabbages, a garlic, an oven, a\\n  carrot, a head of broccoli, a potato, a stalk of celery, a lettuce head, and a toaster.\\n  How many vegetables do I have?\\nExample: 'Let''s list and count each vegetable individually:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (1)\\n\\n  4. Cabbage (2)\\n\\n  5. Garlic\\n\\n  6. Carrot\\n\\n  7. Broccoli\\n\\n  8. Potato\\n\\n  9. Celery\\n\\n  10. Lettuce\\n\\n\\n  Now, let''s verify the count:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (1)\\n\\n  4. Cabbage (2)\\n\\n  5. Garlic\\n\\n  6. Carrot\\n\\n  7. Broccoli\\n\\n  8. Potato\\n\\n  9. Celery\\n\\n  10. Lettuce\\n\\n\\n  Total number of vegetables: 10\\n\\n\\n  Answer: 10'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1635.33it/s]\n",
      "Evaluating step(65): 0.96 across 50 samples, Max potential: 0.96: 100%|██████████| 50/50 [00:23<00:00,  2.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pass validation: 0.96 > 0.94\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 100/100 [00:00<00:00, 3294.35it/s]\n",
      "Evaluating step(65): 0.95 across 100 samples, Max potential: 0.95: 100%|██████████| 100/100 [00:39<00:00,  2.51it/s]\n",
      "Training Step: 66:  42%|████▏     | 5/12 [01:50<02:42, 23.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 66\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 186.04it/s]\n",
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    },
    {
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     "text": [
      "Trace with id fe9b883c-4f47-44f7-a388-b03a2fb10413 already exists. Updating the trace.\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\rTraining:  50%|█████     | 2/4 [00:01<00:01,  1.30it/s]"
     ]
    },
    {
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     "output_type": "stream",
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      "Trace with id 12a6ff3d-f54d-4d89-b5f0-1aec30e96398 already exists. Updating the trace.\n",
      "Trace with id 840d9ed5-8222-45a9-a406-7445feae9733 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Trace with id 3a9a47c8-a210-43a4-8d24-b9159babb6e4 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 636.54it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 9420.11it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 111.34it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.50it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 321.28it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 731.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['99607986-e107-46b8-b86b-177b295983c4']\n",
      "New prompts: [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=\"input_str: I have a yam, a cauliflower, a bed, two cabbages, a garlic, an oven, a\\n  carrot, a head of broccoli, a potato, a stalk of celery, a lettuce head, and a toaster.\\n  How many vegetables do I have?\\nExample: 'Let''s list and count each vegetable individually:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (1)\\n\\n  4. Cabbage (2)\\n\\n  5. Garlic\\n\\n  6. Carrot\\n\\n  7. Broccoli\\n\\n  8. Potato\\n\\n  9. Celery\\n\\n  10. Lettuce\\n\\n\\n  Now, let''s verify the count:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (1)\\n\\n  4. Cabbage (2)\\n\\n  5. Garlic\\n\\n  6. Carrot\\n\\n  7. Broccoli\\n\\n  8. Potato\\n\\n  9. Celery\\n\\n  10. Lettuce\\n\\n\\n  Total number of vegetables: 10\\n\\n\\n  Answer: 10'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 1120.89it/s]\n",
      "Evaluating step(66): 0.96 across 50 samples, Max potential: 0.96: 100%|██████████| 50/50 [00:00<00:00, 212.00it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.96 <= 0.96, revert\n",
      "Training Step: 67\n"
     ]
    },
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      "\n",
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     ]
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      "\rTraining:  25%|██▌       | 1/4 [00:01<00:05,  1.78s/it]"
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     "output_type": "stream",
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      "Trace with id d71ad721-d21d-42f1-af9b-719ff026406b already exists. Updating the trace.\n"
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     ]
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      "\n",
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      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 48.64it/s]\n",
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      "\n",
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      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 8608.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['99607986-e107-46b8-b86b-177b295983c4']\n",
      "New prompts: [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=\"input_str: I have a yam, a cauliflower, a bed, two cabbages, a garlic, an oven, a\\n  carrot, a head of broccoli, a potato, a stalk of celery, a lettuce head, and a toaster.\\n  How many vegetables do I have?\\nExample: 'Let''s list and count each vegetable individually:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (1)\\n\\n  4. Cabbage (2)\\n\\n  5. Garlic\\n\\n  6. Carrot\\n\\n  7. Broccoli\\n\\n  8. Potato\\n\\n  9. Celery\\n\\n  10. Lettuce\\n\\n\\n  Now, let''s verify the count:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (1)\\n\\n  4. Cabbage (2)\\n\\n  5. Garlic\\n\\n  6. Carrot\\n\\n  7. Broccoli\\n\\n  8. Potato\\n\\n  9. Celery\\n\\n  10. Lettuce\\n\\n\\n  Total number of vegetables: 10\\n\\n\\n  Answer: 10'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 4202.88it/s]\n",
      "Evaluating step(67): 0.96 across 50 samples, Max potential: 0.96: 100%|██████████| 50/50 [00:00<00:00, 405.51it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.96 <= 0.96, revert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 68:  67%|██████▋   | 8/12 [02:02<00:47, 11.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 68\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 77.30it/s]\n",
      "Training:   0%|          | 0/4 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id d4194dd1-739a-4509-8ac8-7c3f89649ee7 already exists. Updating the trace.\n"
     ]
    },
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      "Training:  75%|███████▌  | 3/4 [00:01<00:00,  2.62it/s]"
     ]
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    {
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      "Trace with id 1eb770ed-ff6f-481e-8c16-b9749a44a1a6 already exists. Updating the trace.\n"
     ]
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     ]
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      "Trace with id 54e272c5-1360-462e-b773-4c58c61472ee already exists. Updating the trace.\n"
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    {
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      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 10831.00it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 179.03it/s]\n",
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      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 502.04it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 639.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['99607986-e107-46b8-b86b-177b295983c4']\n",
      "New prompts: [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=\"input_str: I have a yam, a cauliflower, a bed, two cabbages, a garlic, an oven, a\\n  carrot, a head of broccoli, a potato, a stalk of celery, a lettuce head, and a toaster.\\n  How many vegetables do I have?\\nExample: 'Let''s list and count each vegetable individually:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (1)\\n\\n  4. Cabbage (2)\\n\\n  5. Garlic\\n\\n  6. Carrot\\n\\n  7. Broccoli\\n\\n  8. Potato\\n\\n  9. Celery\\n\\n  10. Lettuce\\n\\n\\n  Now, let''s verify the count:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (1)\\n\\n  4. Cabbage (2)\\n\\n  5. Garlic\\n\\n  6. Carrot\\n\\n  7. Broccoli\\n\\n  8. Potato\\n\\n  9. Celery\\n\\n  10. Lettuce\\n\\n\\n  Total number of vegetables: 10\\n\\n\\n  Answer: 10'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 3305.62it/s]\n",
      "Evaluating step(68): 0.96 across 50 samples, Max potential: 0.96: 100%|██████████| 50/50 [00:00<00:00, 539.54it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.96 <= 0.96, revert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 69:  75%|███████▌  | 9/12 [02:09<00:32, 10.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 69\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 84.70it/s]\n",
      "Training:  25%|██▌       | 1/4 [00:01<00:03,  1.26s/it]"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Trace with id 0e8910c8-703d-4766-a483-c5691125fd03 already exists. Updating the trace.\n",
      "Trace with id 74d1bc97-46cd-406d-8c3a-2f999aae1b2f already exists. Updating the trace.\n",
      "Trace with id 701be0ee-29e0-42f5-be04-72d2b73e3968 already exists. Updating the trace.\n"
     ]
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      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.67it/s]\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Trace with id de4e75d6-a21b-4004-925d-a9a818bd0f7c already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 331.49it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 488.36it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 274.35it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.51it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 596.31it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 14678.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['000a3738-1f09-40b0-9f8b-2dec63a3f7f8']\n",
      "New prompts: [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=\"input_str: I have two heads of broccoli, an apple, a lettuce head, and two onions.\\n  How many vegetables do I have?\\nExample: 'Let''s list each item individually and determine if it is a vegetable:\\n\\n\\n  1. Two heads of broccoli (vegetables)\\n\\n  2. An apple (not a vegetable)\\n\\n  3. A lettuce head (vegetable)\\n\\n  4. Two onions (vegetables)\\n\\n\\n  Now, let''s count the vegetables:\\n\\n\\n  1. Two heads of broccoli\\n\\n  2. One lettuce head\\n\\n  3. Two onions\\n\\n\\n  Total number of vegetables:\\n\\n  2 (broccoli) + 1 (lettuce) + 2 (onions) = 5\\n\\n\\n  Answer: 5'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 4604.98it/s]\n",
      "Evaluating step(69): 0.5 across 4 samples, Max potential: 0.96:   8%|▊         | 4/50 [00:00<00:00, 88.47it/s]\n",
      "Training Step: 70:  92%|█████████▏| 11/12 [02:13<00:08,  8.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.4 <= 0.96, revert\n",
      "Training Step: 70\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 169.70it/s]\n",
      "Training:  25%|██▌       | 1/4 [00:01<00:03,  1.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id e2bfbbe0-fb79-4df5-9a7d-50c9085947bc already exists. Updating the trace.\n",
      "Trace with id 6c34d6e5-0e3d-4243-834e-fd6c5883f467 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 1b4b3ab0-d20f-4fc2-a09c-4592a227a8e5 already exists. Updating the trace.\n",
      "Trace with id aefd17e5-9682-4420-a820-c484a63d6dcd already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
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      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 262.75it/s]\n",
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      "\n",
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      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1091.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['99607986-e107-46b8-b86b-177b295983c4']\n",
      "New prompts: [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=\"input_str: I have a yam, a cauliflower, a bed, two cabbages, a garlic, an oven, a\\n  carrot, a head of broccoli, a potato, a stalk of celery, a lettuce head, and a toaster.\\n  How many vegetables do I have?\\nExample: 'Let''s list and count each vegetable individually:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (1)\\n\\n  4. Cabbage (2)\\n\\n  5. Garlic\\n\\n  6. Carrot\\n\\n  7. Broccoli\\n\\n  8. Potato\\n\\n  9. Celery\\n\\n  10. Lettuce\\n\\n\\n  Now, let''s verify the count:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (1)\\n\\n  4. Cabbage (2)\\n\\n  5. Garlic\\n\\n  6. Carrot\\n\\n  7. Broccoli\\n\\n  8. Potato\\n\\n  9. Celery\\n\\n  10. Lettuce\\n\\n\\n  Total number of vegetables: 10\\n\\n\\n  Answer: 10'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 4087.46it/s]\n",
      "Evaluating step(70): 0.96 across 50 samples, Max potential: 0.96: 100%|██████████| 50/50 [00:00<00:00, 345.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.96 <= 0.96, revert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 71: 100%|██████████| 12/12 [02:17<00:00,  6.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 71\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 87.52it/s]\n",
      "Training:  25%|██▌       | 1/4 [00:01<00:03,  1.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 6c0d3a9a-bb01-4fb3-a68b-1edf66861235 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\rTraining:  50%|█████     | 2/4 [00:01<00:01,  1.37it/s]"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "Trace with id 234e39df-1bc4-41df-a515-895cb2614a53 already exists. Updating the trace.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\rTraining:  75%|███████▌  | 3/4 [00:01<00:00,  1.92it/s]"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "Trace with id 8895d6bd-eab0-48af-ad4b-51f8007258b1 already exists. Updating the trace.\n"
     ]
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      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.87it/s]\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "Trace with id 4dad0f65-d624-48c2-a795-596c00b0535a already exists. Updating the trace.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 262.50it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 10369.11it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 43.64it/s]\n",
      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.75it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 321.11it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1141.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['99607986-e107-46b8-b86b-177b295983c4']\n",
      "New prompts: [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=\"input_str: I have a yam, a cauliflower, a bed, two cabbages, a garlic, an oven, a\\n  carrot, a head of broccoli, a potato, a stalk of celery, a lettuce head, and a toaster.\\n  How many vegetables do I have?\\nExample: 'Let''s list and count each vegetable individually:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (1)\\n\\n  4. Cabbage (2)\\n\\n  5. Garlic\\n\\n  6. Carrot\\n\\n  7. Broccoli\\n\\n  8. Potato\\n\\n  9. Celery\\n\\n  10. Lettuce\\n\\n\\n  Now, let''s verify the count:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (1)\\n\\n  4. Cabbage (2)\\n\\n  5. Garlic\\n\\n  6. Carrot\\n\\n  7. Broccoli\\n\\n  8. Potato\\n\\n  9. Celery\\n\\n  10. Lettuce\\n\\n\\n  Total number of vegetables: 10\\n\\n\\n  Answer: 10'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 4407.91it/s]\n",
      "Evaluating step(71): 0.96 across 50 samples, Max potential: 0.96: 100%|██████████| 50/50 [00:00<00:00, 397.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.96 <= 0.96, revert\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Step: 72: : 13it [02:23,  6.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Step: 72\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 113.31it/s]\n",
      "Training:  25%|██▌       | 1/4 [00:00<00:02,  1.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 85d63f78-39c0-4753-a9fc-52202df48673 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\rTraining:  50%|█████     | 2/4 [00:01<00:01,  1.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 433650a5-ca75-4867-b235-3af4a7c55c67 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\rTraining:  75%|███████▌  | 3/4 [00:01<00:00,  2.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id e250f80e-334e-4f85-ac1f-df9a2013d578 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 1d3eceeb-ad24-40f6-8752-2f38241172cb already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 170.72it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 13981.01it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 195.45it/s]\n",
      "Training: 100%|██████████| 4/4 [00:01<00:00,  2.46it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 241.42it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 322.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['000a3738-1f09-40b0-9f8b-2dec63a3f7f8']\n",
      "New prompts: [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=\"input_str: I have two heads of broccoli, an apple, a lettuce head, and two onions.\\n  How many vegetables do I have?\\nExample: 'Let''s list each item individually and determine if it is a vegetable:\\n\\n\\n  1. Two heads of broccoli (vegetables)\\n\\n  2. An apple (not a vegetable)\\n\\n  3. A lettuce head (vegetable)\\n\\n  4. Two onions (vegetables)\\n\\n\\n  Now, let''s count the vegetables:\\n\\n\\n  1. Two heads of broccoli\\n\\n  2. One lettuce head\\n\\n  3. Two onions\\n\\n\\n  Total number of vegetables:\\n\\n  2 (broccoli) + 1 (lettuce) + 2 (onions) = 5\\n\\n\\n  Answer: 5'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 3970.90it/s]\n",
      "Evaluating step(72): 0.5 across 4 samples, Max potential: 0.96:   8%|▊         | 4/50 [00:00<00:00, 96.75it/s] \n",
      "Training Step: 73: : 14it [02:30,  6.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.4 <= 0.96, revert\n",
      "Training Step: 73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 73.23it/s]\n",
      "Training:  25%|██▌       | 1/4 [00:01<00:05,  1.97s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id daa5804f-1aad-4f01-b26c-6b31c57f065f already exists. Updating the trace.\n",
      "Trace with id dd9d8748-4926-4bcd-902d-6a4c5cb38267 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 04e77795-cc9b-4530-a883-5f775e3fbc76 already exists. Updating the trace.\n",
      "Trace with id 1f682cab-026c-4803-8018-a45d027aa026 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 211.00it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1551.58it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 205.19it/s]\n",
      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.80it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 266.20it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1059.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['000a3738-1f09-40b0-9f8b-2dec63a3f7f8']\n",
      "New prompts: [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=\"input_str: I have two heads of broccoli, an apple, a lettuce head, and two onions.\\n  How many vegetables do I have?\\nExample: 'Let''s list each item individually and determine if it is a vegetable:\\n\\n\\n  1. Two heads of broccoli (vegetables)\\n\\n  2. An apple (not a vegetable)\\n\\n  3. A lettuce head (vegetable)\\n\\n  4. Two onions (vegetables)\\n\\n\\n  Now, let''s count the vegetables:\\n\\n\\n  1. Two heads of broccoli\\n\\n  2. One lettuce head\\n\\n  3. Two onions\\n\\n\\n  Total number of vegetables:\\n\\n  2 (broccoli) + 1 (lettuce) + 2 (onions) = 5\\n\\n\\n  Answer: 5'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 2226.42it/s]\n",
      "Evaluating step(73): 0.5 across 4 samples, Max potential: 0.96:   8%|▊         | 4/50 [00:00<00:00, 91.13it/s]\n",
      "Training Step: 74: : 16it [02:35,  6.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.4 <= 0.96, revert\n",
      "Training Step: 74\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 93.65it/s]\n",
      "Training:  25%|██▌       | 1/4 [00:01<00:04,  1.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id 945f82c7-03d9-4f49-8267-be7abac2bce6 already exists. Updating the trace.\n",
      "Trace with id a9a202f5-e723-4d24-ae5e-ad1084a52ef8 already exists. Updating the trace.\n",
      "Trace with id d46e538c-832d-4eb5-ba9b-a308f666baba already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trace with id b538075d-01af-4b76-b835-9005f3044609 already exists. Updating the trace.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 115.74it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 1086.11it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 225.28it/s]\n",
      "Training: 100%|██████████| 4/4 [00:02<00:00,  1.50it/s]\n",
      "\n",
      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 224.88it/s]\n",
      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 14242.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sampled_augmented_demos: ['b538075d-01af-4b76-b835-9005f3044609']\n",
      "New prompts: [PromptData(id='327b63f0-b532-435a-85d7-6137d4e52c4c', name='llm_counter.system_prompt', data=\"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\", requires_opt=True), PromptData(id='73a3953b-6351-44d8-a36f-7521db346cca', name='llm_counter.few_shot_demos', data=\"input_str: I have a blackberry, a raspberry, a peach, a head of broccoli, a plum,\\n  an orange, two bananas, a grape, two garlics, a nectarine, a lettuce head, and an\\n  apple. How many fruits do I have?\\nExample: 'Let''s list each item and identify whether it is a fruit:\\n\\n\\n  1. Blackberry - Fruit\\n\\n  2. Raspberry - Fruit\\n\\n  3. Peach - Fruit\\n\\n  4. Head of broccoli - Not a fruit\\n\\n  5. Plum - Fruit\\n\\n  6. Orange - Fruit\\n\\n  7. Two bananas - Fruits (2 bananas)\\n\\n  8. Grape - Fruit\\n\\n  9. Two garlics - Not fruits\\n\\n  10. Nectarine - Fruit\\n\\n  11. Lettuce head - Not a fruit\\n\\n  12. Apple - Fruit\\n\\n\\n  Now, let''s count the fruits:\\n\\n\\n  1. Blackberry\\n\\n  2. Raspberry\\n\\n  3. Peach\\n\\n  4. Plum\\n\\n  5. Orange\\n\\n  6. Two bananas (counted as 2)\\n\\n  7. Grape\\n\\n  8. Nectarine\\n\\n  9. Apple\\n\\n\\n  Total number of fruits:\\n\\n  1 + 1 + 1 + 1 + 1 + 2 + 1 + 1 + 1 = 10\\n\\n\\n  Answer: 10'\", requires_opt=True)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading Data: 100%|██████████| 50/50 [00:00<00:00, 634.93it/s]\n",
      "Evaluating step(74): 0.5 across 4 samples, Max potential: 0.96:   8%|▊         | 4/50 [00:28<05:25,  7.07s/it]\n",
      "Training Step: 74: 100%|██████████| 12/12 [03:12<00:00, 16.04s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail validation: 0.4 <= 0.96, revert\n",
      "Saved ckpt to /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n",
      "Training time: 484.17421078681946s\n",
      "ckpt_file: /content/adalflow/ckpt/ObjectCountAdalComponent/constrained_max_steps_12_4e8a1_run_1.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train(\n",
    "    debug=False,\n",
    "    max_steps=12,\n",
    "    strategy=\"random\",\n",
    "    raw_shots=0,\n",
    "    bootstrap_shots=1,\n",
    "    resume_from_ckpt=ckpt_path,\n",
    "    exclude_input_fields_from_bootstrap_demos=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xTB4lO3PFPnP"
   },
   "source": [
    "Finally, we got 96% on the val and 95% on the test!!! This is really close to GPT4o's performance. This took us 72 steps!\n",
    "\n",
    "The score is consistent, meaning this is a good prompt.\n",
    "Here is our final optimized prompt:\n",
    "\n",
    "System:\n",
    "\n",
    "```\n",
    "\n",
    "\"prompt\": [\n",
    "                {\n",
    "                    \"id\": \"327b63f0-b532-435a-85d7-6137d4e52c4c\",\n",
    "                    \"name\": \"llm_counter.system_prompt\",\n",
    "                    \"data\": \"You will answer a reasoning question. Carefully count each item and verify your total. List each item individually and ensure accuracy. Show your calculations step by step. The last line of your response should be: 'Answer: $VALUE' where VALUE is a numerical value.\",\n",
    "                    \"requires_opt\": true\n",
    "                },\n",
    "                {\n",
    "                    \"id\": \"73a3953b-6351-44d8-a36f-7521db346cca\",\n",
    "                    \"name\": \"llm_counter.few_shot_demos\",\n",
    "                    \"data\": \"input_str: I have a yam, a cauliflower, a bed, two cabbages, a garlic, an oven, a\\n  carrot, a head of broccoli, a potato, a stalk of celery, a lettuce head, and a toaster.\\n  How many vegetables do I have?\\nExample: 'Let''s list and count each vegetable individually:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (1)\\n\\n  4. Cabbage (2)\\n\\n  5. Garlic\\n\\n  6. Carrot\\n\\n  7. Broccoli\\n\\n  8. Potato\\n\\n  9. Celery\\n\\n  10. Lettuce\\n\\n\\n  Now, let''s verify the count:\\n\\n\\n  1. Yam\\n\\n  2. Cauliflower\\n\\n  3. Cabbage (1)\\n\\n  4. Cabbage (2)\\n\\n  5. Garlic\\n\\n  6. Carrot\\n\\n  7. Broccoli\\n\\n  8. Potato\\n\\n  9. Celery\\n\\n  10. Lettuce\\n\\n\\n  Total number of vegetables: 10\\n\\n\\n  Answer: 10'\",\n",
    "                    \"requires_opt\": true\n",
    "                }\n",
    "            ]\n",
    "```\n",
    "\n",
    "\n",
    "You will see all steps record from the log."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fr0V3XNCHAis"
   },
   "source": [
    "Happy Optimizing!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Wnvqs3RyI_z"
   },
   "source": [
    "# Issues and feedback\n",
    "\n",
    "If you encounter any issues, please report them here: [GitHub Issues](https://github.com/SylphAI-Inc/LightRAG/issues).\n",
    "\n",
    "For feedback, you can use either the [GitHub discussions](https://github.com/SylphAI-Inc/LightRAG/discussions) or [Discord](https://discord.gg/ezzszrRZvT)."
   ]
  }
 ],
 "metadata": {
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  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
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